AWS re:Invent 2021: Our Predictions Vs Announcements for ML Services

AWS re:Invent 2021: Our Predictions Vs Announcements for ML Services

Based on the current trends and advancements in the technology industry, in addition to several other factors, we made certain predictions about new services / features that were likely to be launched at the AWS re:Invent 2021 annual conference. The table below presents a wrap-up of all our predictions about the event in comparison with the actual announcements made by AWS:

S.No

Idexcel’s Predictions

AWS re:Invent 2021 Announcements

1

Release of new generation ec2 instances for faster Machine Learning Training and Inference, which will offer a better Price Performance Ratio.

AWS announced 3 new Amazon EC2 instances powered by AWS-designed chips. They are as follows:

(i) Amazon EC2 C7g instances powered by new AWS Graviton3 processors that provide up to 25% better performance for compute-intensive workloads over current generation C6g instances powered by AWS Graviton2 processors.

(ii) Amazon EC2 Trn1 instances powered by AWS Trainium chips which provide the best price performance and the fastest time to train most Machine Learning models in Amazon EC2.

2

Amazon Textract will soon penetrate the market by providing extraction solutions that are domain specific, covering specific types of document extraction solutions. We may see examples of specific types of documents that will be extracted.

Amazon Textract had announced specialized support for automated processing of identity documents. Users can now swiftly and accurately extract information from IDs (eg. U.S. Driver Licenses & Passports) which have varying templates or formats.

3

Improvements in Lex are likely to be out later this year or early next year, with the recent acquisition of Wickr.

AWS announced the Amazon Lex Automated Chat Bot Designer (in Preview), a new feature that simplifies the process of chatbot training and design by bringing in a level of automation to it.

4

A range of Automation options within AWS Service are likely to be announced.

Amazon SageMaker Inference Recommender – A new capability of SageMaker introduced at AWS re:Invent 2021, which lets users choose the best available compute instance and configuration to deploy machine learning models for optimal inference performance and cost. Also, it minimizes the time taken to obtain Machine Learning (ML) models in production by automating performance benchmarking and load testing models across SageMaker ML instances. Users can now utilize Inference Recommender to deploy their model to a real-time inference endpoint that delivers the finest performance at a meanest cost.

Recap of Swami Sivasubramanian’s Keynote Announcements at AWS re:Invent 2021

What To Expect From AWS reInvent 2021

Amazon Web Services (AWS) has announced a heap of features and services to make technologies like Machine Learning more effective and economical, along with a new USD $ 10 million scholarship programme for careers in Machine Learning (ML).

During his 2-hour keynote session at re:Invent 2021, which is in Day-3 of returning to Las Vegas after a one-year interval due to the pandemic, Vice President (VP), Amazon AI at AWS, Swami Sivasubramanian, revealed new solutions to make Machine Learning more approachable and inexpensive, in addition to new training programmes to further democratize the technology and make it simpler to experiment with. Also, AWS announced several new capabilities for its Machine Learning service i.e. Amazon SageMaker. This combines strong new capabilities, which include a no-code environment for building accurate ML predictions, more precise data labelling utilizing highly skilled annotators, and a universal Amazon SageMaker Studio notebook experience for better association across domains. We hereby present below, a summary of Sivasubramanian’s biggest announcements:

  • Amazon DevOps Guru for RDS tool lets you automatically detect, diagnose and resolve complicated database-related (Amazon Aurora databases) issues within minutes. Also, the DevOps Guru for RDS can help rectify a wide range of issues, such as over-exploitation of host resources, database bottlenecks or misbehavior of SQL queries. Whenever an issue is detected, users can view them either through the DevOps Guru console or via notifications from Amazon EventBridge or Amazon Simple Notification Service (SNS).
  • AWS Database Migration Service Fleet Advisor lets you accelerate database migration with automated inventory and migration recommendations. This tool is specifically designed to help make it easier and quicker to get your data to the cloud and match it with the appropriate database service. DMS Fleet Advisor spontaneously builds an inventory of your on-prem database and analytics service by streaming data from on-prem to Amazon S3.
  • New SageMaker Studio Notebook service allows users to access a broad range of data sources and conduct data engineering, analytics, and Machine Learning workflows in one notebook. Currently, Amazon SageMaker Studio has the capability to integrate directly to EMR, the company’s Hadoop-based service that grants access to frameworks such as Spark, Presto, MapReduce, and Hive. Now, SageMaker Studio users can build, terminate, manage, discover and connect to EMR clusters directly from within their SageMaker Studio environment, which would in turn streamline workflows for data scientists.
  • Amazon Sagemaker Studio Lab is a free service for students and other learners or developers to experiment and learn Machine Learning. There are things like JupyterLab IDE, 15 GB of storage, and model can be trained on GPUs. After training the model, the user can also deploy the model in AWS Infrastructure just by one-click using Sagemaker capabilities.
  • Amazon SageMaker Ground Truth Plus allows users to deliver high-quality training databases fast, with no necessity to write a single line of code. Basically, this is a professional services version of SageMaker Ground Truth, which already exists. This new service empowers users to associate themselves with a pool of expert data labelers who have been curated by AWS, and to have the data labeling process directly incorporated with their SageMaker environment. Also, this new service offering can bring down data labeling costs by up to 40%.
  • Amazon SageMaker Platform is getting 3 new innovations:
    • SageMaker Training Compiler is a new feature that can accelerate the training of deep learning models by up to 50% through more efficient use of GPU instances.
    • SageMaker Inference Recommender helps users to choose the best available compute instance and configuration to deploy Machine Learning models for ideal inference performance and cost. This new feature can reduce the time to deploy from weeks to hours.
    • SageMaker Serverless Inference is a new inference option that empowers users to easily deploy machine learning models for inference without having to configure or manage the underlying infrastructure. This new feature can lower the cost of ownership with pay-per-use pricing.
  • Amazon Kendra Experience Builder allows you to deploy a fully functional and customizable search experience with Amazon Kendra in just a few clicks, with absolutely no necessity for any coding or Machine Learning experience. Experience Builder service delivers an intuitive visual workflow to swiftly build, customize and initiate your Kendra-powered search application, safely on the cloud. You can begin with the ready-made search experience template in the builder, that can be tailored by simply dragging and dropping the components you require, like filters or sorting.
  • Amazon Lex Automated Chatbot Builder is a new capability which reduces the time and effort it takes for customers and partners to design a chatbot from weeks to hours, by simply automating the process using existing conversation transcripts. It is indeed an easy and intuitive way of designing chatbots, by employing advanced natural language comprehension driven by deep learning techniques. Amazon Lex enables you to build, test and deploy chatbots and virtual assistants on contact center services (i.e. Amazon Connect), websites and messaging platforms (e.g. Facebook Messenger). The automated chatbot designer widens the usability of Amazon Lex to the design phase. It utilizes Machine Learning to render an initial bot design that you can then refine and initiate conversational experiences quicker.

Schedule a meeting with our AWS Cloud Solution Experts and accelerate your cloud journey with Idexcel.

AWS re:Invent Recap: Amazon SageMaker Pipelines

What happened?

The new service, Amazon SageMaker Pipelines, has been launched to provide continuous integration and delivery pipelines that automate steps of ML (Machine Learning) workflows. It’s the first CI/CD service for ML to build, store, and track automated workflows and also create an audit trail for training data and modeling configurations.

Why is it important?

  • Ease of Use: It has built in ML workflow templates that can be used to build, test, and deploy ML models quickly.
  • Compliance: Amazon SageMaker pipeline logs can be saved as audit trails to recreate models for similar future business cases that help support compliance requirements.
  • Better Capabilities: This service brings CI/CD practices to ML, which keeps the development & production environments separate, version control measures, on-demand testing, and end-to-end automation.
  • Automation: As the first Purpose-built CI/CD service for ML that incorporates automation of data loading, transformation, training, tuning, and deployment workflow steps, this increases productivity significantly.
  • Scalability: With the ability to create, automate, and manage end-to-end ML workflows at scale, there’s peace of mind knowing various are stored and be referred back to for audit purposes, compliance requirements, and future solution builds.

Why We’re Excited

Amazon SageMaker Pipelines offer a more efficient and productive solution to scale by reusing the workflow steps created and stored in a central repository. With built-in templates to deploy, train, and test models, our ML teams can quickly leverage CI/CD in our ML environments and easily incorporate models we’ve already created. With the SageMaker Pipelines model registry, we can track these model versions in one central location that gives us visibility and up to date record logs of the best possible solution options to meet client deployment needs.

If you’re looking to explore these services further and need some guidance, let us know and we’ll connect you to an Idexcel expert!

How To Build Business Intelligent Chatbots with Amazon Lex


Enabling Business Intelligence in Chatbots with Amazon Lex

In this fast-paced digital age, organizations need a fast and efficient way of gathering information. Especially in a customer-driven market like fintech, “time is money ”. Decisions will have to be made accurately and fast. Incorrect decisions can lead to severe consequences or lost customers. In several fintech applications, information is made available through reporting solutions, presentations, charts, etc. What customers find difficult is digging out the specific report or data needed through a multitude of mouse-clicks and then spending a lot of time analyzing them. There is a critical need for one central point from which a variety of data can be delivered to the user in an efficient and effective process. AWS technology and tools open several avenues to make this possible.

Amazon Lex – Machine Learning As a Service

Amazon Lex is one service that enables state-of-the-art chatbots to be built. It has redefined how people in the industry perceive building chat-bots. Bots themselves have gradually evolved from typical question-answering bots to more complex ones that can perform an array of functions. Amazon Lex offers features that tackle several complexities faced while building the previous generation of chatbots. The intent fulfillment, dialogue flow, and context management features of Amazon Lex help to make conversation with a chat-bot as human-like as possible.

This blog discusses how information can be retrieved from databases with a simple question asked to Kasper (the name of our bot). The following components of this blog will give a clear understanding to the user, how everything is built, networked, and coupled with a custom user interface.

Solution Architecture

Kasper is a chatbot built specifically for a lending platform to retrieve various data points based on specific inquiries. Like all bots, Kasper is also built on intents, utterances, and slots. After adding intents, its corresponding utterances, and slots, a few slots need to be added as custom slots. For example, there was a query – “show clients where invoice amount is greater than 20000”.  In the utterance section of Kasper, it was recorded as below:

 

Here ‘cola’ and ‘operatora’ are slot variables under custom slots ‘columnname’ and ‘operator’ respectively.

Natural Language to SQL Conversion

All the responses that require output from the database are sourced with the help of a lambda function. The JSON response from the lambda function contains the input transcript, intent, and slots information. The back-end application then receives the response from the lambda function, segregates the JSON, and classifies information into the corresponding intent and slots. The application extracts the slots and intents and then proceeds to build the query.

Responses from Kasper

Responses from Kasper can result in different formats of data. There can be single value responses, images, tables, etc. The types of responses are automatically determined from the intents. A custom website with a chat window has been developed for interacting with Kasper. The chat window can take in both texts, as well as audio inputs. The following are the detailed sections explaining each response type, with their corresponding chat window.

 

Response type I – Single values

There are instances where users might want to know about a sum or count or any other single value response. For example, an inquiry might be “count the number of clients whose due date is within 2 weeks” or “sum of the invoice amount of all clients“. The responses of these queries will be just a single value eg. “10,000”.

Response type II – Images and Tables

1. Tables

Images and tables are the next type of responses Kasper delivers. Once the SQL query is constructed, it connects with the database and retrieves data and stores it in a pandas dataframe. This dataframe can be exported as an html table for previewing through the chat window. It can also be downloaded in the form of a csv file.

2. Images

From the pandas dataframe, different charts/graphs can be derived. When an image response is expected, charts are generated using python libraries, saved to a file, and then exported to the chat window. Two types of images are generated – one is a thumbnail and the second is the actual image. Kasper is equipped with a feature named Auto-visualization. According to the dataframe, the function will decide what type of graph or chart has to be plotted. There are numerous rules applied before making that decision. For example, the function determines whether a specific column features continuous or categorical values. The resulting graph is plotted based on such combinations.

Response type III – Fallback mechanism with response card

The third type of response are response cards – a response to clarify the intention of the user. Suppose the user asks an ambiguous question like this “what is the amount of Apollo Inc. “. The chatbot will find the query to be missing some keywords because the user did not specify the type of amount (either invoice amount or balance amount). Kasper then prompts back with a list of possible options, so the user can select the appropriate option and receive the accurate result.

Kasper is a chatbot that has evolved to its current operational capabilities because of maximizing Amazon Lex’s potential and accommodating other significant AWS services to its architecture. Currently, Kasper can solve important natural language to SQL problems and a few FAQ questions as well. It can also be modified for other domain problems to suit specific needs. Over time, more capabilities will be possible to add and could serve as a first-line substitute for human support personal, freeing up your support team to help address more critical issues more quickly. If you’re interested in how a chatbot might improve your operations, schedule a Free assessment with our Machine Learning team today.

Want to learn more?

Is Machine Learning the Solution to Your Business Problem?

The term Machine Learning (ML) is defined as ‘giving computers the ability to learn without being explicitly programmed’ (this definition is attributed to Arthur Samuel)Another way to think of this is that the computer gains intelligence by identifying patterns and data sets on its own, improving output accuracy over time as more data sets are examined. Since ML can be a challenging solution to implement, we’ve put together some foundational steps to assess the feasibility of building an ML solution for your organization: 

1. Identify the problem TYPE 

Start by distinguishing between automation problems and learning problems. Machine learning can help automate your processes, but not all automation problems require learning.

Automation: Implementing automation without learning is appropriate when the problem is relatively straightforward. These are the kinds of tasks where you have a clear, predefined sequence of steps currently being executed by a human, but that could conceivably be transitioned to a machine.

Machine Learning: For the second type of problem, standard automation is not enough – it requires learning from data. Machine learning, at its core, is a set of statistical methods meant to find patterns of predictability in datasets. These methods are great at determining how certain features of the data are related to the outcomes you are interested in.

2. Determine if you have the right data

The data might come from you, or an external provider. In the latter case, make sure to ask enough questions to get a good feel for the data’s scope and whether it is likely to be a good fit for your problem. consider your ability to collect it, its source, the required format, where it is stored, but also the human factor. Both executives and employees involved in the process need to understand its value and why taking care of its quality is important. 

3. Evalute Data Quality and Current State

Is the data you have usable as-is, or does it require manual human manipulation before introducing into the learning environment? A solid dataset is one of the most important requirements for building a successful machine learning model. Machine learning models that make predictions to answer their questions usually need labeled training data. For example, a model built to learn how to determine borrower due dates to improve accurate reporting needs a starting point from which to build an accurate ML solution. Labeled training datasets can be tricky to obtain and often require creativity and human labor to create them manually before any ML can happen.

4. Assess Your Resources

Do you have the right resources to maintain your ML solution? Once you have an appropriate question and a rich training dataset in hand, you’ll need people with experience in data science to create your models. Lots of work goes into figuring out the best combination of features, algorithms, and success metrics needed to make an accurate model. This can be time-consuming and requires consistent maintenance over time.

5. Confirm Feasibility of ML Project

With the four previous steps for assessing whether or not ML is right for your organization, consider the responses. Is the question appropriate for building an ML business solution? Is the data available, or at least attainable? Does the data need hours of human labor? Do you have the right skilled team members to carry out the project? And finally, is it worth it – meaning, will the solution have a large impact, financially and socially? 

It’s important to consider these key questions when assessing whether or not Machine Learning is the right solution for your organization’s needs. Connect with our ML experts today to schedule your free assessment. 

How can Artificial Intelligence and Machine Learning Help with DevOps?

How can Artificial Intelligence and Machine Learning Help with DevOps?

Artificial Intelligence (AI) and Machine Learning (ML) have both become integral parts within the world of DevOps because of their ability to help developers break free from the chains of manual labor. DevOps is all about breaking down siloed developmental walls, and there is no doubt that AI and ML can help teams achieve their goal. With the combination of both these practices, efficiency and productivity can be further enhanced by providing additional performance to businesses.

How will Artificial Intelligence and Machine Learning Drive DevOps in the Future?

AI and ML are undoubtedly the best ways to drive efficiency and growth within processes; however, they do come with their own set of problems. The idea behind the implementation of these practices is to help organizations achieve their targets; however, what’s difficult is the fact that the application of the technologies into a company’s workflow might not be as easy as it seems.

To get AL and ML up and running within your business, you’ll need creative developers, who are well versed with the nuances of the two practices. Given this knowledge, it might be preferred to state upfront that the implementation of AI and ML will initially be quite a tedious task and that the learning curve would be slower than usual.

The above does not negate the fact that DevOps developers can still gain a lot of traction by adopting the essential features of Artificial Intelligence and Machine Learning within their day to day functions.

Through the successful implementation of AI and ML, management can expect to make rapid decisions, which can significantly benefit the business and further lead to improved profitability within the company.

To add a futuristic touch to the world of DevOps, AI and ML can help manage large volumes of data and solve computational problems. AI will eventually become the sole driver to assess, compute, and ease decision making within DevOps environments.

What is Artificial Intelligence’s Influence on DevOps?

Artificial Intelligence is the changing face of DevOps; it can change the way DevOps teams develop their tools, deliver their production goals, and deploy the changes within their functions. AI can mainly help developers improve an application’s efficiency, and enhance business operations.

To understand the influence of both practices, it’s best to summarize:

Improved Data Accessibility
Within the DevOps environment, data access is a big concern. However, this issue is addressed, when AI releases critical data from its formal storage place. Through the use of AI, data can be collected from different sources and made available in a single spot, which can then further be used for different types of analysis and production uses.

Greater Ease of Implementation
AI is all about self-implementing systems; this means, the transition of processes from human run systems to mechanical systems is seamless and smooth. When it comes to assessing human efficacy, one can understand how quickly system complexity is driven out.

Effective Use of Resources
Through the use of Artificial Intelligence, resources can be managed effectively, and judiciously, wherever needed.

How can Artificial Intelligence and Machine Learning be Applied to Optimize DevOps?

Organizations have come a long way, especially when it comes to technical transformations. DevOps and its implementation is no stranger to this concept. Couple the ideas of AI and ML with your organization’s technology hierarchy, and you can rest assured that you have a winning solution on your hands.

AL can also help create complex data pipelines which feed data into app development models. By the dawn of 2020, if predictions are to be believed, AI and ML will take the lead, and digital transformation will see the launch of a new technical era. However, like the two sides of a coin, even AI and ML don’t come without their own set of issues and drawbacks. To derive maximum benefit out of a DevOps structure, a customized DevOps stack is needed.

AI and ML, as futuristic concepts, have taken over the world of technology by storm. The combination of the two languages can go a long way in ensuring a steady ROI for an organization while enhancing the working of IT operations. Efficiency can take an all-new stage, and productivity can reach another level, if DevOps, AI, and ML can be fused together into one dependent model.

Also Read

The Effect of Artificial Intelligence on the Evolution of Technology
The Future of Machine Learning
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The Future of Machine Learning

Technology is innovating and revolutionizing the world at a rapid pace with the application of Machine Learning. Machine learning (ML) and Artificial Intelligence (AI) might appear to be the same, but the reality is that ML is an application of AI that enables a system to automatically learn from data input. The functional capabilities of ML drive operational efficiency and capacity automation in various industries.

Technological Innovation for Convenience
Workforce handling is tedious and less productive; this is where Artificial Intelligence has lucratively overcome the age-old system of manual labor. With the world moving at such a fast pace, monitoring has become a constraint for most organizations; for this very reason, Artificial Intelligence and Machine Learning are used more as tools of convenience rather than just pieces of technology.

We have seen how accounting systems have replaced ledger books. At the same time, processes have been set up to align machines with organizational requirements effectively to balance everyone’s demands.

However, with the way Artificial Intelligence is advancing, it seems this technology is quickly going to change the way processes are functioning. Not only trends on social media will be affected, but even marketing will see a complete makeover through the use of Artificial Intelligence.

The Effect on Various Fields
When it comes to Artificial Intelligence, everybody wants a taste of it. From marketing experts and tech innovators to education sector decision-makers, Artificial Intelligence holds the capability to pave the path for a healthy future. Artificial Intelligence has been designed to provide utmost customer satisfaction. To derive maximum results from the nuances of AI customer-centric processes will need to align their business metrics to the logic of this latest technology.

As Big Data evolves, machine learning will continue to grow with it. Digital Marketers are wrapping their heads around Artificial Intelligence to produce the most efficient results by putting in minimal efforts. The entire algorithm and the build of Artificial Intelligence will be used to predict trends and analyze customers. These insights are aimed at helping marketers build patterns to drive organizational results. In the future, it seems like every basic customer need would be taken care of through fancy automation and robotic algorithms.

Healthcare Sector
The healthcare industry is one of the widely reckoned industries in the world today. Simply put, it has the maximum effect on today’s society. Through the use of Artificial Intelligence and Machine Learning, doctors are hoping to be able to prevent the deadliest of diseases, which even includes the likes of cancer and other life-shortening diseases.

Robots Assistants, intelligent prostheses, and other technological advancements are pushing the health care sector into a new frenzy, which will be earmarked towards progressing into a constantly evolving future.

Financial Sector
In the financial sector, it’s vital to ensure that companies can secure their operations by reducing risk and increasing their profits. Through the use of extensive Artificial Intelligence, companies can build elaborate predictive models, which can successfully mitigate the potential of on-boarding risky clients and processes; this can include signing on dangerous clients, taking on risky payments, or even signing up on hazardous loans.

No matter what might be the company’s requirement, Artificial Intelligence is a one-stop shop when it comes to preventing fraudulent activities in day to day operations – this, in turn, will lead to money savings possibilities, profit enhancement and risk reduction within every organizational vertical.

Robotics
We are steadily heading towards a future that will be marked complete with the rise of robotics and automation; this is not going to be restricted to the medical sector only; intelligent drones, manufacturing facilities, and other industries are also going to be benefited by the rise of robotics. Artificial Intelligence methodologies like Siri and Cortana have already seen the light of day – this is just the beginning. More and more companies are going to take these capabilities to a new level.

As more and more military operations begin to seek advantages from the likes of mechanized drones, it won’t be long before e-commerce companies like Amazon start to deliver their products through the use of drones. The potential is endless, and so are the possibilities. In the end, it is all about using technology in the right manner to ensure the appropriate benefits are driven in the right direction.

Also Read

How Artificial Intelligence Transforming Finance Industry
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Big Data Empowers AI & Machine Learning

Big Data Empowers AI & Machine Learning
Recent decades have witnessed a rapid growth in technological advancement. From raising budget-tight efficiency to rendering the smart sensing technology, IT industries not only contest for the top spot but also play a vital role in transforming the world as we perceive it. Artificial Intelligence (AI) is not an unusual term nowadays, but the importance bestowed upon it is somewhat undernourished. Coupling the technology with other recent technological advancements, AI can be optimized at even higher levels. Big data is another growing area whose full potential is still unknown. So far, IT has de-duced numerous benefits of big data interplay, but, those seem to be just a fraction of the lucrative repertoire big data has in its lap.

A new strategy, where Big Data is employed in AI, turns out to be a total game changer. Best in its class, Big Data, which uses customer and organization generated information to help firms make better decisions concerning efficiency and cost-effectiveness, meets one of the best technological feats that humankind has achieved—AI, and we can all guess the possible results.

AI can perform such complex tasks which involve sensory recognition and decision-making that ordi-narily require human intelligence. The advent of robotics has further introduced an autonomy that re-quires no human intervention in the implementation of those decisions. Such a technology when paired with Big Data, can rise to unforeseen immensities that we cannot presently articulate. Howev-er, some of the primary outcomes of this merging are as follows:

Soaring Computational power
With continually emerging modern processors, millions of bits of information can be processed in a second or less. Additionally, graphics processors also contribute exponentially to the rising CPS (calcu-lations per second) rate of processors. With the help of Big Data analytics, the processing of big vol-umes of data, and the rendering of rules for machine learning, on which AI will operate, is possible in real time.

Cost Effective and Highly Reliable Memory Devices
Memory and storage are the essential components of any computing machine, and their health de-termines the overall strength of the computer. Efficient storage and quick retrieval of data are critical for a device to work smartly, even more so for AI.

Memory devices such as Dynamic RAMs and flash memories are increasingly in demand for they make use of information merely for processing and not for storage. Data, thus, doesn’t become centralized in one computer but is instead accessed from the cloud itself. With the aid of Big Data, memories of more precise knowledge could be built, which will inevitably result in better surface realities. Addition-ally, the ready cloud which indulges into this large-scale computation is used to produce the AI knowledge space. With the better memory of information, indeed, higher AI learning will be imparted along with reduced costs.

Machine Learning From Non-Artificial Data
Big Data is proven to be a source of genuine business interaction. Big data accumulated for analytics provide a better grounding for prospects of actions and planning of the organizations. Earlier, AI was used to deduce learning from the samples fed in the storage of the machine, but with Big Data analyt-ics it is now possible to provide machine learning with “real” data which helps AI perform better and more accurately.

Improved Recognition Algorithms
With technological advancements, it has become possible to program AI machines in such a way that they can make sense of what we say to them almost as if they were humans. However, humans can produce an infinite set of sentences through combinations based upon underlying linguistic and per-ceptive analysis. Big Data is also capable of empowering AI in the same way as it can form algorithms that the human brain possesses. The voluminous data renders a broad base for building algorithmic analysis, which in turn enhances the quality of AI perception. Alexa, HomePod, Google Home, and other virtual assistants are good (if not the best) examples of improved recognition in AI.

Promoting Open-Source Programming Languages
In the past, due to cloud unavailability (thereby unavailable Big Data), AI data models could use only simple programming languages. These scripting languages such as Python or Ruby where excellent for statistical data analysis, but with the help of Big Data, additional programming tools for data can be uti-lized.

With the introduction of new developments in technology such as Big Data, the scope, and future of AI has been soaring in new dimensions. With the merging of Big Data analytics and AI, we can create a highly efficient, reliable, and dependable in its nature AI defined infrastructure.

Also Read

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