Reinforcing this transformative potential, substantial research highlights the significant benefits of AI adoption. A recent extensive study projected that countries with widespread AI integration could experience a staggering 26% surge in their GDP by 2035. Furthermore, this same study anticipates a remarkable $15.7 trillion augmentation in global revenue and savings by 2030, all attributable to the profound impact of AI. Embracing generative AI technologies offers knowledge workers and business leaders a spectrum of new opportunities, propelling organizations to maintain competitiveness within the dynamic marketplace while achieving heightened efficiency, innovation, and growth.

While specific AI solutions are increasingly tailored to sectors such as financial services and healthcare, the most profound and widespread applications of AI manifest in general-purpose capabilities, significantly elevating the productivity and efficiency of professionals across industries, this horizontal domain has witnessed the surge of generative AI’s prominence over the last six months, as it garners attention for its immense potential in enhancing productivity, forging a new technological trajectory that leverages the collective knowledge of the world for individual tasks.

THE PROMISE OF GENERATIVE AI IN REDEFINING WORK

HARNESSING THE VALUE OF GENERATIVE AI AMIDST CHALLENGES

The ability of generative AI to effortlessly craft valuable, meticulously synthesized content like text and images from minimal prompts has evolved into an essential business capability, meriting provision to a vast array of knowledge workers. My research and investigation show that generative AI can accelerate work tasks by 1.3x to 5x, enhancing speed and efficiency. Additionally, there are intangible yet equally significant benefits in fostering innovation, embracing diverse perspectives, and managing opportunity costs. Generative AI’s prowess extends to producing high-value content such as code or formatted data, domains traditionally demanding specialized expertise and training. It can undertake sophisticated assessments of intricate, domain-specific materials, spanning legal documents to medical diagnoses.

In essence, contemporary generative AI services signify a tipping point, poised to deliver substantial value across various work scenarios, democratizing access to advanced capabilities for average workers.

However, prudence is imperative, as a chorus of cautionary voices underscores the underlying challenges. While AI is a potent force, it necessitates careful consideration to exploit its potential while mitigating its inherent risks, encompassing:

Addressing Data Bias: The effectiveness of generative AI models hinges on their training data, perpetuating biases if they’re present. This could inadvertently perpetuate unfavorable practices or exclude specific groups.

Enhancing Model Interpretability: The intricacies of generative AI models render their outcomes complex and challenging to decipher, potentially eroding trust in decision-making. This obscurity could be resolved as these models evolve.

Mitigating Cybersecurity Threats: Like any technology processing sensitive data, generative AI models are susceptible to cyber threats such as hacking, breaches, and input manipulation. Stringent measures are necessary to safeguard these systems and the associated data.

Navigating Legal and Ethical Considerations: Deploying generative AI in decision-making contexts such as hiring or lending necessitates alignment with legal and ethical standards. Ensuring compliance and safeguarding privacy is paramount.

Balancing AI Reliance: Overdependence on AI models can diminish human judgment and expertise. A balanced approach that values human input and AI’s enhancements is vital.

Sustaining Maintenance and Ethical Usage: Sustaining generative AI models demands ongoing upkeep, with businesses requiring the resources and infrastructure to manage and maintain them effectively. Addressing the energy consumption of these models is also imperative.

SEIZING THE POWER OF AI IN THE WORKPLACE

While challenges persist, the allure of AI’s benefits remains steadfast. As evidence accumulates, indicating the tangible outcomes of generative AI solutions, organizations must proactively institute operational, management, and governance frameworks that underpin responsible AI integration.

CRUCIAL STEPS IN DEPLOYING GENERATIVE AI AT WORK

Promulgating Clear AI Guidelines: Establish clear guidelines and policies for AI tool usage, emphasizing data privacy, security, and ethical considerations, fostering transparent use.

Empowering via Education and Training: Give employees thorough education and training to use AI tools effectively and morally while fostering a lifelong learning culture.

Structuring AI Governance: Implement robust governance frameworks for overseeing AI tool utilization, delineating responsibility, communication channels, and checks and balances.

Oversight and Vigilance: Ingrain mechanisms for continual oversight and monitoring of AI tools, ensuring compliance with guidelines, consistent model application, and unbiased outcomes.

Promoting Partnership and Feedback: Develop a collaborative workplace by fostering employee feedback and sharing best practices, resulting in a vibrant learning environment.

Enforcing Ethical Guidelines: Formulate ethical AI guidelines that prioritize transparency, fairness, and accountability, guiding the responsible use of AI tools.

Conducting Ethical Impact Assessments: Prioritize ethical impact assessments by deploying AI tools, addressing potential risks, and aligning means with moral principles.

Guarding Against Bias: Monitor AI tools for biases throughout development and deployment, ensuring fair and equitable outcomes.

Ensuring Transparency and Accordance: Furnish transparency about AI tool operations, decisions, and data usage, promoting understanding and trust.

Balancing Human and AI Expertise: Strike the proper equilibrium between AI augmentation and human expertise, preventing overreliance on AI’s capabilities.

These steps encompass a comprehensive approach to AI integration, capitalizing on AI’s power while mitigating its challenges. As organizations advance along the AI adoption curve, an encompassing ModelOps framework and the proper internal functions can be the bedrock for these practices.

FOUNDATION MODELS: THE KEYSTONE OF AI ENABLEMENT

To empower the workforce with AI-driven tools, organizations often turn to models that seamlessly generate valuable results without demanding significant user effort or training. Foundation models like Large Language Models (LLMs) are ideal candidates for powering AI work tools due to their extensive training in vast textual knowledge.

Vendors offering LLM-based work tools take distinct paths, either optimizing proprietary models or utilizing well-established models like OpenAI’s GPT-4. The prevailing foundation models encompass a diverse array of industry adoptions, including:

• AI21’s Jurassic-2
• Anthropic’s Claude
• Cohere’s Language Models
• Google’s Pathways Language Model (PaLM)
• Hugging Face’s BLOOM
• Meta’s LLaMA
• NVIDIA’s NeMo
• OpenAI’s GPT-3.5 and GPT-4

The selection of an appropriate model is integral to comprehending capabilities, safety measures, and potential risks, fostering informed decisions.

PIONEERING AI-ENABLED TOOLS FOR THE WORKFORCE

A gamut of AI-powered tools finds their basis in foundation models, synthesizing business content and insights. While many AI tools span various creative niches, the focus narrows to foundation model-powered, text-centric, and horizontally applicable tools, extending their utility to diverse professionals across industries. This list showcases AI tools that possess substantial potential for broader work contexts:

Bard – Google’s foray into the LLM-based knowledge assistant domain.

ChatGPT – The pioneer of general-purpose knowledge assistance, initiating the generative AI revolution.

ChatSpot – HubSpot’s content and research assistant, catering to marketing, sales, and operation’s needs.

Docugami – AI is bolstering business document management through specialized foundation models.

Einstein GPT – Salesforce’s content, insights, and interaction assistant, amplifying platform capabilities.

Google Workspace AI Features – Google’s integration of generative AI features into its productivity suite.

HyperWrite – A business writing assistant streamlining content creation.

Jasper for Business – An intelligent writing creator, ensuring brand consistency for external content.

Microsoft 365 Copilot/Business Chat – AI-assisted content generation and contextual user-data-driven business chatbots.

Notably – An AI-enhanced business research platform.

Notion AI – A business-ready content and writing assistant.

Olli – AI-powered enterprise-grade analytics and BI dashboards.

Poe by Quora – A knowledge assistant chatbot harnessing Anthropic’s AI models.

Rationale – An AI-powered tool aiding business decision-making.

Seenapse – AI-aided business ideation, propelling innovation.

Tome – An AI-driven tool for crafting PowerPoint presentations.

WordTune – A versatile writing assistant fostering content creation.

Writer – AI-based writing assistance, enhancing writing capabilities.

These tools encompass a broad spectrum of AI-enabled functionalities, focusing on text-based content and insights. While the landscape is evolving, with vertical AI solutions gaining traction, this list captures the essence of generative AI’s transformational impact on diverse facets of work.

In the journey toward the Future of Work, forthcoming explorations will delve into AI solutions tailored to specific industries, such as HR, healthcare, and finance. If you represent an AI-for-business startup utilizing foundation models and catering to enterprise clientele, I welcome you to connect. Engage for AI-in-the-workplace insights, advisory, and more.

Wrapping Up

The potential of generative AI, exemplified by ChatGPT, is poised to revolutionize how we approach work in diverse industries. As research consistently highlights the significant benefits of AI adoption, it becomes clear that businesses embracing these technologies will enhance their efficiency and innovation and contribute to a global landscape of unprecedented progress. With the ability to automate intricate tasks and tap into a wealth of collective knowledge, generative AI opens up exciting new horizons for professionals and businesses, positioning them to thrive in an ever-evolving marketplace. This transformative wave promises economic growth and a future of work marked by creativity, efficiency, and boundless opportunity.

The post The Transformative Impact Of Generative AI On The Future Of Work appeared first on Indium.

“Kubeflow is an open-source project created to help deployment of ML pipelines. It uses components as python functions for each step of pipeline. Each component runs on the isolated container with all the required libraries. It runs all the components in the series one by one.”

In this article we are going to train a custom machine learning model on Vertex AI using Kubeflow Pipeline.

About Dataset

Credit Card Customers dataset from Kaggle will be used. The 10,000 customer records in this dataset include columns for age, salary, marital status, credit card limit, credit card category, and other information. In order to predict the customers who are most likely to leave, we must analyse the data to determine the causes of customer churn.

Interesting Read: In the world of hacking, we’ve reached the point where we’re wondering who is a better hacker: humans or machines.

Let’s Start

Custom Model Training

Step 1: Getting Data

We will download the dataset from GitHub. There are two csv files in the downloaded dataset called churner_p1 and churner_p2, I have created a Big Query dataset credit_card_churn with the tables as churner_p1 and churner_p2 with this csv files. I have also created the bucket called credit-card-churn on Cloud Storage. This bucket will be used to store the artifacts of the pipeline

Step 2: Employing Workbench

Enable the Notebook API by going to Vertex AI and then to the Workbench section. Then select Python 3 by clicking on New Notebook. Make sure to choose the us-central1 region.

It will take a few minutes to create the Notebook instance. Once the notebook is created click on the Open JupyterLab to launch the JupyterLab.

We will also have to enable the following APIs from API and services section of Vertex AI.

1. Artifact Registry API
2. Container Registry API
3. AI Platform API
4. ML API
5. Cloud Functions API
6. Cloud Build API

Now click on the Python 3 to open a jupyter notebook in the JupyterLab Notebook section and run the below code cells.

USER_FLAG = “–user”

!pip3 install {USER_FLAG} google-cloud-aiplatform==1.7.0

!pip3 install {USER_FLAG} kfp==1.8.9

This will install google cloud AI platform and Kubeflow packages. Make sure to restart the kernel after the packages are installed.

import os

PROJECT_ID = “”

# Get your Google Cloud project ID from gcloud

if not os.getenv(“IS_TESTING”):

    shell_output=!gcloud config list –format ‘value(core.project)’ 2>/dev/null

    PROJECT_ID = shell_output[0]

    print(“Project ID: “, PROJECT_ID)

Create the variable PROJECT_ID with the name of project.

BUCKET_NAME=”gs://” + PROJECT_ID

BUCKET_NAME

Create the variable BUCKET_NAME, this will return the same bucket name we have created earlier.

import matplotlib.pyplot as plt

import pandas as pd

from kfp.v2 import compiler, dsl

from kfp.v2.dsl import pipeline, component, Artifact, Dataset, Input, Metrics, Model, Output, InputPath, OutputPath

from google.cloud import aiplatform

# We’ll use this namespace for metadata querying

from google.cloud import aiplatform_v1

PATH=%env PATH

%env PATH={PATH}:/home/jupyter/.local/bin

REGION=”us-central1″

PIPELINE_ROOT = f”{BUCKET_NAME}/pipeline_root/”

PIPELINE_ROOT

This will import required packages and create the pipeline folder in the credit-card-churn bucket.

#First Component in the pipeline to fetch data from big query.

#Table1 data is fetched

@component(

    packages_to_install=[“google-cloud-bigquery==2.34.2”, “pandas”, “pyarrow”],

    base_image=”python:3.9″,

    output_component_file=”dataset_creating_1.yaml”

)

def get_data_1(

   bq_table: str,

   output_data_path: OutputPath(“Dataset”)

):

    from google.cloud import bigquery

    import pandas as pd

    bqclient = bigquery.Client()

   table = bigquery.TableReference.from_string(

      bq_table

    )

    rows = bqclient.list_rows(

        table

    )

   dataframe = rows.to_dataframe(

        create_bqstorage_client=True,

    )

   dataframe.to_csv(output_data_path)

The first component of the pipeline will fit the data from the table churner_p1 from big query and pass the csv file as the output for the next component. The structure is the same for every component. We have used the @component decorator to install the required packages and specify the base image and output file, then we create the get_data_1 function to get the data from big query.

#Second Component in the pipeline to fetch data from big query.

#Table2 data is fetched

#First component and second component doesnt need inputs from any components

@component(

    packages_to_install=[“google-cloud-bigquery==2.34.2”, “pandas”, “pyarrow”],

    base_image=”python:3.9″,

    output_component_file=”dataset_creating_2.yaml”

)

def get_data_2(

    bq_table: str,

    output_data_path: OutputPath(“Dataset”)

):

   from google.cloud import bigquery

   import pandas as pd

    bqclient = bigquery.Client()

   table = bigquery.TableReference.from_string(

       bq_table

    )

   rows = bqclient.list_rows(

        table

    )

    dataframe = rows.to_dataframe(

        create_bqstorage_client=True,

    )

    dataframe.to_csv(output_data_path)

The second component of the pipeline will fit the data from the table churner_2 from big query and pass the csv file as the output for the next component. The first component and second component do not need inputs from any components.

#Third component in the pipeline to to combine data from 2 sources and for some data transformation

@component(

    packages_to_install=[“sklearn”, “pandas”, “joblib”],

   base_image=”python:3.9″,

  output_component_file=”model_training.yaml”,

)

def data_transformation(

    dataset1: Input[Dataset],

    dataset2: Input[Dataset],

    output_data_path: OutputPath(“Dataset”),

):

    from sklearn.metrics import roc_curve

    from sklearn.model_selection import train_test_split

    from joblib import dump

    from sklearn.metrics import confusion_matrix

    from sklearn.tree import DecisionTreeClassifier

    from sklearn.ensemble import RandomForestClassifier

   import pandas as pd

    data1 = pd.read_csv(dataset1.path)

    data2 = pd.read_csv(dataset2.path)

    data=pd.merge(data1, data2, on=’CLIENTNUM’, how=’outer’)

    data.drop([“CLIENTNUM”],axis=1,inplace=True)

   data = data.dropna()

   cols_categorical = [‘Gender’,’Dependent_count’, ‘Education_Level’, ‘Marital_Status’,’Income_Category’,’Card_Category’]

    data[‘Attrition_Flag’] = [1 if cust == “Existing Customer” else 0 for cust in data[‘Attrition_Flag’]]

    data_encoded = pd.get_dummies(data, columns = cols_categorical)

    data_encoded.to_csv(output_data_path)

The third component is where we have combined the data from the first and second component and did the data transformation such as dropping the “CLIENTNUM” column, dropping the null values and converting the categorical columns into numerical. we will pass this transformed data as csv to the next component.

#Fourth component in the pipeline to train the classification model using decision Trees or Randomforest

@component(

    packages_to_install=[“sklearn”, “pandas”, “joblib”],

    base_image=”python:3.9″,

    output_component_file=”model_training.yaml”,

)

def training_classmod(

    data1: Input[Dataset],

   metrics: Output[Metrics],

    model: Output[Model]

):

    from sklearn.metrics import roc_curve

    from sklearn.model_selection import train_test_split

    from joblib import dump

    from sklearn.metrics import confusion_matrix

    from sklearn.ensemble import RandomForestClassifier

    import pandas as pd

    data_encoded=pd.read_csv(data1.path)

    X = data_encoded.drop(columns=[‘Attrition_Flag’])

    y = data_encoded[‘Attrition_Flag’]

   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=100,stratify=y)

   model_classifier = RandomForestClassifier()

    model_classifier.fit(X_train,y_train)

    y_pred=model_classifier.predict(X_test)

    score = model_classifier.score(X_test,y_test)

    print(‘accuracy is:’,score)

    metrics.log_metric(“accuracy”,(score * 100.0))

    metrics.log_metric(“model”, “RandomForest”)

    dump(model_classifier, model.path + “.joblib”)

In the fourth component we will train the model with the Random Classifier and we have used the “accuracy” as the evaluation metric.

@component(

   packages_to_install=[“google-cloud-aiplatform”],

    base_image=”python:3.9″,

    output_component_file=”model_deployment.yaml”,

)

def model_deployment(

    model: Input[Model],

    project: str,

    region: str,

    vertex_endpoint: Output[Artifact],

   vertex_model: Output[Model]

):

    from google.cloud import aiplatform

   aiplatform.init(project=project, location=region)

    deployed_model = aiplatform.Model.upload(

        display_name=”custom-model-pipeline”,

      artifact_uri = model.uri.replace(“model”, “”),

        serving_container_image_uri=”us-docker.pkg.dev/vertex-ai/prediction/sklearn-cpu.0-24:latest”

    )

    endpoint = deployed_model.deploy(machine_type=”n1-standard-4″)

    # Save data to the output params

    vertex_endpoint.uri = endpoint.resource_name

    vertex_model.uri = deployed_model.resource_name

Fifth component is the last component, in this we will create the endpoint on the Vertex AI and deploy the model. We have used Docker as base IMAGE and have deployed the model on “n1-standard-4” machine.

@pipeline(

    # Default pipeline root. You can override it when submitting the pipeline.

    pipeline_root=PIPELINE_ROOT,

    # A name for the pipeline.

    name=”custom-pipeline”,

)

def pipeline(

   bq_table_1: str = “”,

    bq_table_2: str = “”,

    output_data_path: str = “data.csv”,

    project: str = PROJECT_ID,

    region: str = REGION

):

    dataset_task_1 = get_data_1(bq_table_1)

   dataset_task_2 = get_data_2(bq_table_2)

   data_transform=data_transformation(dataset_task_1.output,dataset_task_2.output)

    model_task = training_classmod(data_transform.output)

    deploy_task = model_deployment(model=model_task.outputs[“model”],project=project,region=region)

In the last we have pipeline function which will call all the components in the sequential manner: dataset_tast_1 and dataset_tast_2 will get the data from the big query, data_transform will transform the data, model_task will train the Random Classifier model and deploy_task will deploy the model on Vertex AI.

compiler.Compiler().compile(pipeline_func=pipeline, package_path=”custom-pipeline-classifier.json”)

Compiling the pipeline.

run1 = aiplatform.PipelineJob(

    display_name=”custom-training-vertex-ai-pipeline”,

    template_path=”custom-pipeline-classifier.json”,

    job_id=”custom-pipeline-rf8″,

   parameter_values={“bq_table_1”: “credit-card-churn.credit_card_churn.churner_p1″,”bq_table_2”: “credit-card-churn.credit_card_churn.churner_p2”},

   enable_caching=False,)

Creating the pipeline job.

run1.submit()

Running the pipeline job.

With this we have completed creating the Kubeflow pipeline and we can see it on the Pipelines section of Vertex AI.

Our Pipeline has run successfully and we have managed to get 100% accuracy for the classification.

We can use this model to get the online prediction using Rest API or Python. We can also create the different pipelines and compare their metrics on Vertex AI.

With this we have completed the project and learned how to create the Pipeline on Vertex AI for custom train models.

I hope you will find it useful.

See you again.

The post Kubeflow Pipeline on Vertex AI for Custom ML Models appeared first on Indium.

Augmented analytics is gaining relevance in direct proportion to the increasing volume of data businesses are generating but are unable to leverage in real-time. Large investments are needed into tools and technologies for analytics teams to be able to draw insights on the one hand. On the other, business users are dependent on the experts to generate the reports to make decisions. But this can be a bottleneck and render all the data useless due to the delays. Therefore, they need self-service capabilities with customizable report generation to suit their requirements and with sufficient controls to minimize security risks.

Check this out: Support Your Analytics and BI Efforts for the Next 10 Years with a Modern Data Ecosystem

Augmented analytics helps overcome these limitations by automating data preparation, delivering insights faster, and facilitating information-sharing collaboration for greater success.

Benefits of Augmented Analytics

Augmented analytics can help businesses make data-backed decisions, respond faster to trends and issues, and improve customer satisfaction. It can strengthen competitive advantage by becoming a key differentiator and breaking barriers to innovation. Some of the benefits of augmented analytics include:

• Greater Agility: With augmented analysis, the data quality improves, thereby increasing the agility and responsiveness of the business. With augmented analytics, data cleaning, blending, and transforming from multiple sources becomes easier and accelerates insight generation.
• 360-deg View of Data: Augmented analysis facilitates a holistic view of data by providing the details, statistics, and insights that enable segregating the irrelevant from the important ones. This helps decision-makers focus on vital insights that can help develop critical strategies and dynamic capacities. A better overview of data allows data scientists to assess the datasets of clients and create elaborate client profiles to identify loyal customers. It can also help with identifying trends and creating strategies to leverage the trends.
• Information-Backed Decision-Making: Rather than rely on gut feel, which has a high chance of failing in a highly dynamic business environment, augmented analytics provides decision makers with insights that improve the quality and outcome of decisions. It also helps with discovering new queries hitherto not obvious to create new opportunities for growth and efficiency.
• Accelerating Decision Making: The insights also enable businesses to respond quickly to insights and trends in real time. Self-service further empowers function heads to identify issues and improve performance quickly, without having to rely on IT team or data scientists. By automating the data management and analytics processes, business users can also improve their productivity and focus on innovation. This removes the chances of human errors, thus enhancing the quality of decisions.
• Cost-Efficiency: Introducing efficiencies, cutting down on waste, reducing errors, and faster decision-making are some of the direct benefits of augmented analysis. These and many other benefits lead to lowering costs and expenses, optimizing resource utilization, and improving revenue generation.

You might be interested in: Why Augmented Reality Gaming Based Application needs Stress Testing

Features of Augmented Analytics

Some of the key features of augmented analytics that make it the future of analytics include:

• Automated Data Identification: AI enables identifying data attributes and extracting data from a variety of sources, structured and unstructured. This improves the quality of data while expanding the source for deeper insights.
• Use of Statistical Techniques: Statistical algorithms such as forecasting and clustering help with clear insights as well reveal hitherto hidden insights, improving the quality of decision-making. And the good news is, it doesn’t need statisticians or IT experts to reveal these outliers and hidden insights, but are just a click away.
• Faster Data Preparation: Machine Language algorithms help with automated, faster, and smarter data preparation. Clustering or grouping of data is possible based on predetermined criteria, improving the indexing and searching. This also helps with cleaning the data, removing null values, and splitting data fields into different columns.
• Recommendations: Augmented analytics can help create AI-driven recommendation engines to improve data preparation for better discovery, analysis, and sharing.
• Natural Language Interactions: The use of Natural Language Processing services helps with serving queries in natural language, democratizing the query process. Here too, recommendations may pop up suggesting words to improve query quality and gain better insights from the data.

Indium–Enabling Augment Analytics

Indium Software is a cutting-edge data and analytics solution provider with AI and ML capabilities. We work closely with our customers to create AI-based self-learning algorithms whose accuracy improves as errors go down over time. We develop machine learning systems that automate the generation of quick and accurate insights by examining data and learning.

We enable creating intelligent systems that mimic human capabilities and perform repetitive tasks, freeing up resources to focus on innovation and value addition. Using AI-based predictive models, we help our customers create unique services and solutions to address their operational and customer needs better.

We empower our clients to be a step ahead of the competition by:

• Using Natural Language Processing to identify trends, threats, and opportunities.
• Forecasting future trends and prescribing actions using AI/ML-based predictive and prescriptive analytics.

The post <strong>The Future of Augmented Analytics</strong> appeared first on Indium.

The secret to this magnificent growth is the use of technologies that are guided by AI and workflow digitization in the health industry.  We now have multiple healthcare tools, thanks to AI, that provide faster and more efficient medical solutions.

As a medical professional, incorporating AI into your healthcare business will give you the following benefits and more:

• Enhanced efficiency
• Easy access to medical services
• Better data security
• Increased productivity
• Profit maximization and cost reduction

In this article, we will explore the top AI trends that have the most impact on the healthcare industry, explaining the different ways in which they have impacted or changed how medical professionals operate.

Top 7 AI Trends that Transform the Healthcare Industry

1. Robotic Surgeries

Investment in medical devices that use robots has increased greatly with the introduction of AI in the medical industry. The success rate of this trend is so good that it has become the new idea for the future of surgery and is expected to have incredible adoption rates in the coming years.

Surgical robots help identify critical insights and state-of-the-art practices by browsing through millions of data sets with the help of ML techniques. It allows the surgeons to focus on the complex aspects of the surgery.

AI has also been helping surgeons in preoperative planning and intraoperative guidance.

2. Remote Health Monitoring/Telehealth

Telehealth is the process of using technology to get remote health care and help people manage their ailments better without having to go to the hospital. Aside from the innovations for surgery, AI is set to revolutionize how we monitor our health, especially from home. 

AI in Telehealth helps doctors to make data driven decisions, it gives access to real-time data which eventually improves patient experience and health outcomes.  

3. Administrative Workflow Automation

When most people think about healthcare, they only consider the act of treating patients. However, administrative duties are equally important as they determine many productivity factors in any industry or company, irrespective of its niche.

A lot of work is involved in keeping a medical facility running without problems – from getting insurance authorization to following up with patients about medical bills to ensuring useful data is collected and recorded properly. AI helps automate the administrative workflow process and resources to make the system work as efficiently as possible.

4. Digital Therapeutics/Primary Care

Digital therapeutics, otherwise known as DTx, is a proof-based treatment based on patients’ behaviors with the aid of software. Digital therapeutics is expected to improve the healthcare industry when it comes to treatment effectiveness and accessibility. The program observes the feeding, sugar level in the blood, blood pressure, exercise, and medication to improve the treatment of patients.

Medical practitioners and patients can trust the recommendations given through digital therapeutics, which guide the treatment of a couple of common illnesses by using simulations to enhance the changes in behavior. It uses different trackers to change the care of patients without needing professional help from medical practitioners. 

5. Disease Diagnosis and Treatment

AI has been instrumental in the industry, improving the diagnostic and treatment decisions, while reducing medical errors. Integrating AI will give secure access to patient records and data, and it can help detect or define the risks of someone getting a disease.

This will reduce the workload of healthcare providers and help them focus on prevention and treatment for the patients.

6. Drug Discovery

The process, from researching new drugs to getting patients as a viable means of treatment, is long and expensive.

Research of drugs and discovery is another AI trend changing the medical industry rapidly when finding new drugs that can help patients combat infections. The health industry uses the latest in AI development to improve the drug repurposing and discovery process in a way that drastically shortens the time it takes for new drugs to get to the market and also reduces the cost of getting the drugs.

7. Value-Based Care

AI can help drive efficiency of value-based care thus improving the quality of patient care and enhancing patient outcomes.  It helps in organizing and analyzing healthcare data, enhancing diagnostic procedures and predictions, improving informed clinical decision-making and so on.

Medical companies can have significant cost savings utilizing AI.  AI systems process multiple medical records at once, reducing the need for extra workforce and the money they cost. It also helps healthcare professionals make better, well-informed decisions more accurately than before, making it possible for them to give more accurate treatment with reduced risk and the best patient results.

Conclusion

AI has moved the healthcare industry in multiple ways, enhancing the treatment of patients and workflow of professionals.

It is a given that AI will play an even more important role in the medical field as we continue moving toward the future. Healthcare practitioners will continue to integrate modern technologies to make things work better than they already do.

Indium has been helping several healthcare organizations, provide patient-centric business models through seamless digital engineering of advanced technologies that drives superior customer experience and operational efficiencies.

To know more about the services we offer, please click here.

The post Top AI Trends Transforming Healthcare Industry appeared first on Indium.

While data analytics services play a key part in identifying areas of improvements and strengths, unstructured data provides a wealth of information, tapping into which the financial companies can accelerate growth and increase customer delight.

For instance, a financial services company that provides Credit Score Ratings to its customers and helps many banks assess their customers’ credit scores wanted to improve its Know Your Customer process. The company had to process thousands of scanned bank statements to fulfill the KYC requirements for the applicants. The data had to be extracted from scanned images and digital PDFs.

Indium Software built a text extraction model employing its IP-based teX.ai product on 2000 bank statements. It created a scalable pipeline that could handle a large inflow of documents daily. As a result of the automation of the workflow, the processing of a single file took less than a minute, and the company experienced an 80% increase over the method the company employed previously. The accuracy also was nearly 90%.

In another instance, a leading holding conglomerate that capitalizes on fintech and provides financial services to the under-served in Southeast Asia required predictive analytics to be performed to evaluate the creditworthiness and loan eligibility of its customers. The data related to the loan information of the customer and their geographic details were stored in two separate PDFs for each customer, which needed to be merged. In case the customer had taken multiple loans, it had to be summarized at a row level

using business logic and Power BI used to create dashboards to get an overview of the kind of loans, repayment rates, customer churn rate, sales rep performance, and so on.

To predict whether a loan could be offered to a target customer, Indium leveraged tex.ai to extract customer-related loans and geographic details at the row level. This was used to custom-build business logic and summarize the customer-related information at the row level. As a result,

● The pull-through rate increased by 40%

● The loan cycle time decreased by 30%

● The customer acquisition rate went up by 25% within three months

● Application approval rate went up by 40%

● The cost of customer acquisition came down by 20%

Tex.Ai–For Insights from Unstructured Data

Financial services companies have access to many unstructured forms and information. This limits its use in data analytics and reduces efficiency unless it can be accessed in a format where analytics can be run on it to draw insights.

Indium Software’s Tex.ai is a trademark solution that enables customized text analytics by leveraging the ‘organization’s unstructured data such as emails, chats, social media posts, product reviews, images, videos, audio and so on to drive the business forward. It helps to extract data from text, summarize information, and classify content by selecting relevant text data and processing it quickly and efficiently to generate structured data, metadata, and insights.

These insights help to improve:

● Operational agility

● Speed of decision making

● Gaining customer insights

Secure Redaction and Automation

For the financial services industry, Tex.ai’s ability to identify text genres using the intelligent, customizable linguistic application and group similar content helps wade through millions of forms quickly and categorize them with ease. It helps to automate the extraction process, thereby increasing efficiency and accuracy. Tex.ai can also create concise summaries, enabling business teams to obtain the correct context from the right text and improve the quality of insights and decision-making.

Financial services is a sensitive industry regulated by privacy laws. Tex.ai’s redaction tool helps to extract relevant information while masking personal information to ensure security and privacy by masking all personal data.

Check this out: The Critical Need For Data Security And The Role Of Redaction

Tex.ai can also be used to extract insights from chatter and reviews, thereby helping financial institutions create customized products and services and focused promotions to improve conversions and enhance overall customer experience. It can help with fraud detection by analyzing past financial behavior and detecting anomalies, thereby establishing the credibility of the customers. This is especially important for processing loan and credit applications. An added advantage is the software’s ability to support several languages such as English, Japanese, Mandarin, all Latin languages, Thai, Arabic, and so on.

Further, teX.ai provides customizable dashboards and parameters that allow viewing and managing processed documents of customers. An interactive dashboard facilitates monitoring and improvement of processes by identifying and mitigating risks.

Using ok Indium’s Tex.ai solution can help financial services companies to deepen their customer insights, understand their requirements better, and provide bespoke solutions. This will help expand product base, customer base, and revenues while ensuring data privacy and security.

Indium’s team of data analytics experts can also help with customizing the solution to meet the unique needs of our customers.

The post Tex.ai: Harvesting Unstructured Data in the Financial Services Industry to Garner Insights and Automate Workflows appeared first on Indium.

Smart Data Pipelines for Ever-Changing Business Needs

The world today is moving fast, and requirements changing constantly. Businesses need to respond in real-time to improve customer delight and become efficient to become more competitive and grow quickly. In 2020, the global pandemic further compelled businesses to invest in data and database technologies to be able to source and process not just structured data but unstructured as well to maximize opportunities. Getting a unified view of historical and current data became a challenge as they moved data to the cloud while retaining a part in on-premise systems. However, this is critical to understand opportunities and weaknesses and collaborate to optimize resource utilization at low costs.

The concept of the data pipeline is not new. Traditionally, data collection, flow, and delivery happened through batch processing, where data batches were moved from origin to destination in one go or periodically based on pre-determined schedules. While this is a stable system, the data is not processed in real-time and therefore becomes dated by the time it reaches the business user.

Check this out: Multi-Cloud Data Pipelines with Striim for Real-Time Data Streaming

Stream processing enables real-time access with real-time data movement. Data is collected continuously from sources such as change streams from a database or events from sensors and messaging systems. This facilitates informed decision-making using real-time business intelligence. When intelligence is built in for abstracting details and automating the process, it becomes a smart data pipeline. This can be set up easily and operates continuously without needing any intervention.

Some of the benefits of smart data pipelines are that they are:

● Fast to build and deploy

● Fault-tolerant

● Adaptive

● Self-healing

Smart Data Pipelines Based on DataOps Principles

The smart data pipelines are built on data engineering platforms using DataOps solutions. They remove the “how” aspect of data and focus on the 3Ws of What, Who, and Where. As a result, smart data pipelines enable the smooth and unhindered flow of data without needing constant intervention or building or being restricted to a single platform.

The two greatest benefits of smart data pipelines include:

● Instant Access: Business users can access data quickly by connecting the on-premise and cloud environments using modern data architecture.

● Instant Insights: With smart data pipelines, users can access streaming data in real-time to gain actionable insights and improve decisions making.

As the smart data pipelines are built on data engineering platforms, it allows:

● Designing and deploying data pipelines within hours instead of weeks or months

● Improving change management by building resiliency to the maximum extent possible

● Adopting new platforms by pointing to them to reduce the time taken from months to minutes

Smart Data Pipeline Features

Some of the key features of smart data pipelines include:

Data Integration in Real-time: Real-time data movement and built-in connectors to move data to distinct data targets become possible due to real-time integration in smart data pipelines to improve decision-making.

Location-Agnostic: Smart Data Pipelines bridge the gap between legacy systems and modern applications, holding the modern data architecture together by acting as the glue.

Streaming Data to build Applications: Building applications become faster using smart data pipelines that provide access to streaming data with SQL to get started quickly. This helps utilize machine learning and automation to develop cutting-edge solutions.

Scalability: Smart data integration using striim or data pipelines help scale up to meet data demands, thereby lowering data costs.

Reliability: Smart data pipelines ensure zero downtime while delivering all critical workflows reliably.

Schema Evolution: The schema of all the applications evolves along with the business, ensuring keeping pace with changes to the source database. Users can specify their preferred way to handle DDL changes.

Pipeline Monitoring: Built-in dashboards and monitoring help data customers monitor the data flows in real-time, assuring data freshness every time.

Data Decentralization and Decoupling from Applications: Decentralization of data allows different groups to access the analytical data products as needed for their use cases while minimizing disruptions to impact their workflows.

Benefit from indium’s partnership with striim for your data integration requirements: REAL-TIME DATA REPLICATION FROM ORACLE ON-PREM DATABASE TO GCP

Build Your Smart Data Pipeline with Indium

Indium Software is a name to reckon with in data engineering, DataOps, and Striim technologies. Our team of experts enables customers to create ‘instant experiences’ using real-time data integration. We provide end-to-end solutions for data engineering, from replication to building smart data pipelines aligned to the expected outcomes. This helps businesses maximize profits by leveraging data quickly and in real-time. Automation accelerates processing times, thus improving the competitiveness of the companies through timely responses.

The post <strong>Why You Should Use a Smart Data Pipeline for Data Integration of High-Volume Data</strong> appeared first on Indium.

Data downtime encompasses a wide range of problems related to data, such as it being partial, erroneous, inaccurate, having a few null values, or being completely outdated. Tackling the issues with data quality can take up the time of data scientists, delaying innovation and value addition.

Data analysts typically end up spending a large part of their day collecting, preparing and correcting data. This data needs to be vetted and validated for quality issues such as inaccuracy. Therefore, it becomes important to identify, troubleshoot, and fix data quality to ensure integrity and reliability. Since machine learning algorithms need large volumes of accurate, updated, and reliable data to train and deliver solutions, data downtime can impact the success of these projects severely. This can cost companies money and time, leading to revenue loss, inability to face competition, and unable to be sustainable in the long run. It can also lead to compliance issues, costing the company in litigation and penalties.

Challenges to Data Availability

Some common factors that cause data downtime include:

Server Failure: If the server storing the data fails, then the data will become unavailable.

Data Quality: Even if data is available but is inconsistent, incomplete, or redundant, it is as good as not being available.

Outdated Data: Legacy data may be of no use for purposes of ML training.

Failure of Storage: Sometimes, the physical storage device may fail, making data unavailable.

Network Failure: When the network through which the data is accessed fails, data can become unavailable.

Speed of Data Transfers: If the data transfer is slow due to factors such as where data is stored and where it is used, then that can also cause data downtime.

Compatibility of Data: If the data is not compatible with the environment, then it will not be available for training or running the algorithm.

Data Breaches: Access to data may be blocked, or data may be stolen or compromised by malicious factors such as ransomware, causing data loss

Best Practices for Preventing Data Downtime

Given the implications of data downtime on machine learning algorithms, business operations, and compliance, enterprises must ensure the quality of their data. Some of the best practices in avoiding data downtime include:

Create Data Clusters:As storage devices, networks, and systems can fail, creating clusters to spread data will improve availability in case of failures and prevent or minimize data loss. To enable responding to issues at the earliest, tracking and monitoring of availability is also important. The infrastructure should be designed for load balancing and resiliency in case of DDoS attacks.

Accelerate Recovery: Failures are inevitable, and therefore, being prepared for a quick recovery is essential. It could range from troubleshooting to hardware replacement or even restarting the operating systems and database services. It requires the right skills to match the technologies used to speed up the process.

Remove Corrupted Data: Incomplete, incorrect, outdated, or unavailable cause data corruption. Such data cannot be trusted and requires a systematic approach to identify and rectify the errors. The process should be automated and prevent new errors from being introduced.

Improve Data Formatting and Organization: Often, enterprises grapple with the issue of inaccurate data. It is difficult to access and use due to being formatted differently. Deploying tools that can integrate data onto a shared platform is important.

Plan for Redundancy and Backups: Back up data and store it in separate locations or distributed networks to ensure availability and faster restoration in case data is lost or corrupt. Setting up storage devices in a redundant array of independent disks (RAID) configuration is also one approach for this.

Use Tools to Prevent Data Loss: Data breaches and data center damages can be mitigated using data loss prevention tools.

Erasure Coding: In this data protection method, data is broken into fragments, expanded, and then encoded with redundant pieces of data. By storing them across different locations or storage devices, data can be reconstructed from the fragments stored in other locations even if one fails or data becomes corrupted.

Indium to Ensure Your Data Quality

Indium Software is a cutting-edge technology solutions provider with a specialization in data engineering, data analytics, and data management. Our team of experts can work with your data to ensure 24×7 availability using the most appropriate technologies and solutions.

We can design and build the right data architecture to ensure redundancy, backup, fast recovery, and high-quality data. We ensure resilience, integrity, and security, helping you focus on innovation and growth.

Our range of data services include:

Data Engineering Solutions to maximize data fluidity from source to destination BI & Data Modernization Solutions to facilitate data-backed decisions with insights and visualization

Data Analytics Solutions to support human decision-making in combination with powerful algorithms and techniques

AI/ML Solutions to draw far-reaching insights from your data.

FAQs

What is the difference between data quality and accuracy?

Data quality refers to data that includes the five elements of quality:

● Completeness

● Consistency

● Accuracy

● Time-stamped

● Meets standards

Data Accuracy is one of the elements of data quality and refers to the exactness of the information.

Why is data availability important for AI/ML?

Large volumes of high-quality, reliable data is needed to train artificial learning/machine learning algorithms. Data downtime will prevent access to the right kind of data to train algorithms and get the desired result.

The post Avoid Data Downtime and Improve Data Availability for AI/ML appeared first on Indium.

Artificial Intelligence (AI) is transforming nearly every part of human existence, including jobs, the economy, privacy, security, ethics, communication, war, and healthcare. For any technology to prosper in a competitive market, its advantages must exceed its downsides.

As AI research evolves, it is believed that more robots and autonomous systems will replace human jobs. In the 2022 Gartner CIO and Technologies Executive Survey, 48% of CIOs reported that their organizations had already used AI and machine learning technology or planned to do so over the next 12 months. What does this portend for the labour force? As AI helps people become more productive, will it eventually displace them?

This blog examines the advantages of AI services and how their monetization will usher in a new era for humanity.

Why AI?

Artificial intelligence permeates our lives today, making us increasingly dependent on its benefits. Here are some practical ways AI can assist us in doing our jobs more effectively:

Reduce errors

In industries such as healthcare and finance, we can be confident of AI’s outcomes. Imagine the risk of making mistakes when dealing with health concerns or the consequence of a sick patient receiving the incorrect treatment.

AI minimizes the risk of making such errors. The activities performed with AI technologies are accurate. We can successfully use AI in search operations, reduce the likelihood of human negligence, and assist physicians in executing intricate medical procedures.

Simplify difficult tasks

Several undertakings may be impossible for humans to do. But .due to artificial intelligence, we can execute these tasks with minimal effort using robots. Welding is a potentially hazardous activity that AI can carry out. AI can also respond to threats with relative ease, as harmful chemicals and heat have little to no effect on machines.

AI-powered robots can also engage in risky activities such as fuel exploration and marine life discoveries, eliminating human limitations. In conclusion, AI can save innumerable lives.

Provide safety and security

With the help of AI’s computational algorithms, our daily actions have grown safe and secure. To organize and handle data, financial and banking organizations are adopting AI.

Through an intelligent card-based system, AI can also detect fraudulent activity. Even in corporations and offices, biometric systems help track records. Thus, there is a comprehensive record of all modifications, and information is kept safe.

Increase work efficiency

Long working hours are programmed into machines, which increases their production and efficiency. In addition, there is limited potential for error (unlike humans); thus, the outcomes are far more accurate.

Nonetheless, it should be noted that several trials resulted in undesirable reactions from AI-powered apps. When faced with difficult circumstances, robots might become hostile and attack other robots and people. Scientists such as the late Stephen Hawking have long cautioned about the dangers of artificial intelligence and how it affects other living forms. According to numerous AI experts, despite being created by humans, AI can outwit us and usurp our position of authority.

Must Read: Data Is No Longer The New Oil – It Is The World`s Most Valuable Resource

The Dangers Posed by AI

It’s a fact. Without human control, AI can unleash events reminiscent of the most recent sci-fi film! Artificial intelligence can become our overlords and annihilate us with an intellect far beyond our capabilities. AI-powered robots are designed using the computational capabilities of our brains, rendering them purely intelligent beings devoid of human sensitivity, emotion, and vulnerability.

Automation

The concern that robots will replace people on assembly lines and in other basic manufacturing activities has been expressed by thinkers for decades. However, the situation is aggravated by the news that white-collar employment is now at risk.

Ideally, everybody can gain from the greater output with less effort. However, declining employment could compel modern nations to evaluate their present political and economic structures.

Deepfakes

he state of modern social media platforms has increased polarization and the spread of misleading information. In this scenario, the threat of deep fakes has the potential to dilute public knowledge and increase public mistrust.

Artificial intelligence systems have become increasingly capable of generating or editing movies of actual people, with this technology becoming more accessible to the average person.

Data-based AI bias

GIGO, or garbage in, garbage out, is nearly as ancient as computers. It is also a problem we have not been able to solve since it is an existential rather than a technological one.

Manually entering erroneous data into a computer will yield inaccurate results, introducing racial, ethnic, and gender bias into the computing process. When carried out on a worldwide scale, prejudice can reach worrisome dimensions.

Conclusion

The ultimate decision will rely on human beings when we ask ourselves whether AI is a blessing or a burden. We are solely responsible for determining how much control we exercise and whether we use technology for good or ill.

Utilized judiciously, AI can enhance results. Freeing humans from monotonous tasks allows them to be more creative and strategic. When used wisely, AI’s most important contribution will not be to replace but to create. Consequently, AI augmentation—the complementary combination of human and artificial intelligence—might be the most significant “benefit” that AI provides.

Inquire Now to know the capabilities and AI Solutions we offer!

The post Is AI a ‘boon’ or ‘bane’? appeared first on Indium.

A chatbot is a software application powered by AI solutions that communicates with clients in natural language via messaging or voice commands. It can be designed to respond the same way every time to the user, or it can adapt its responses depending on the situation. It can be used via a text messaging application, website chat window, or other social media messaging platforms such as Twitter and Facebook. 

Chatbots play an integral role in e-commerce, allowing businesses to communicate with and engage their customers. Since human presence is not required, chatbots can facilitate information at high speeds. For example, a popular learning platform, Coursera, uses a chatbot to impart an entire course online, with learners conversing and clearing their doubts via the bot.  

Progression of the Chatbot 

While many would agree that chatbots have only recently become a buzzword, the earliest notion dates back to when humans began devising methods to interface with computers. Eliza, the first-ever chatbot launched in 1966 by Joseph Weizenbaum of the MIT Artificial Intelligence Laboratory, was invented even before the personal computer became popular. Eliza assessed the keywords supplied as input before triggering an output according to a defined set of criteria. Several chatbots still employ this approach to produce output. 

Business Benefits of Using Chatbots

A chatbot strengthens customer relationships by interacting with customers and delivering responses. It achieves its market objectives and drives sales promotion processes and service availability. Within some fields, it also provides advantages like decreased operating costs, reduced human error, 24/7 assistance, and automation.

How Chatbots Enable Digital Assurance

Before a chatbot is released in the market, a digital assurance check must be done. It should perceive the customer’s intent and scale appropriately. It is also essential to test it with multiple business cases, input values, and template combinations.

Digital Assurance Workflow

Training Data 

The chatbot must be programmed to comprehend specific mathematical models to determine the user’s purpose. The chat should be tested from the commencement of the use-case test until the conclusion of non-functional components. Chatbot testing is comparable to: 

• Robotic Process Automation (RPA) testing 
• Security Testing and 
• Unified Functional Testing (UFT). 

Pre-launching Tests in Chatbot

Prior to its release, there are three ways to test the chatbot:  

• General test 
• Product/service-specific test 
• Restrictions test

Completing these tests before the release would be ideal since they reveal the status of the main areas and the underlying problems.

General Testing

In the preliminary phase, answering the client’s query is the main criterion. For instance, if a user joins the chat to ask a question, greetings or welcome messages must be displayed on the screen as a first general test. If this does not occur, there is no point in continuing to test.

Domain/Specific Testing

If the first step of welcoming the user has been completed, we move on to the domain test, which involves answering the customer’s queries regarding a unique group or product. This is the conversation stage where your chatbot provides credible information on your products/services. The chatbot should be able to answer coded or frequently asked questions and attain the maximum correct answer ratio. 

Manual Testing

Amazon’s Mechanical Turk, a marketplace that automatically uses human intelligence for testing purposes, reduces the amount of manual labor necessary for manual testing. This strategy increases trust in a product and reduces the number of manual errors.

Limit Testing

Limit testing is the final step of pre-launch testing. In limit testing, the chatbot’s commitment is responding to irrelevant customer information and how effectively it resolves customer matters.

This might interest you: Things to Keep in Mind while Testing Machine Learning Applications

Post Launching Tests in Chatbot

After launching the chatbot, the following tests are required to certify the chatbot is stable and robust enough for optimum utilization.

A/B Testing

A/B testing compares two different types of products to see which product is better. In this test methodology, we must collect the maximum data we can. Though a dated testing model, it is still one of the most effective ways to analyze the efficacy of differences within products.

A/B testing processes two factors:

• Visual Factors: To assess the best designs, colors, and location on the world wide web

• Conversational Factors: To design the quality and performance of an algorithm

Apart from the above factors, there are a few vital aspects and procedures to attain the ideal state of a chatbot, as outlined below.

E2E Testing

This is a very familiar testing technique within QA that ensures real-time user experience and end-to-end flow via text-based or voice-based test coverage.

Natural Language Processing Testing (NLP)

Natural language processing is the linguistically-oriented field of computer science concerned with the software’s ability to understand natural human language, both written and spoken. NLP can define role-based modeling to produce logical algorithms that assess and comprehend a user’s lengthy procedural communication. Natural Language Processing (NLP) Testing evaluates a chatbot’s ability to read the language based on the user’s intent, leading to more natural interactions.

Performance Testing

This testing can involve evaluating concurrent users to ensure the sustainability and quality of responses under varying load conditions, creating a seamless experience for end users.

Security Testing

Chatbots may have access to vast quantities of data or private information. Consequently, they may be a desirable target for hackers, and known web application flaws may also be a security risk for chatbots.

Technological Trends in Chatbots

• Personalization: It has the training to store customer profiles and behavioral status to proceed with upcoming chats. It also suggests profiles or brands using artificial intelligence.
• Machine Learning Operations (MLOps): It uses a CRM to maintain customers’ information which helps to provide customers with details from the previous conversation.
• Voice Recognition: It involves faster intercommunication without having to type, like Alexa, Siri, etc.  

Conclusion

The last few years have seen the profile of chatbots growing exponentially. They have the potential to act as an intuitive link between customers and businesses. The rise of e-commerce and emphasis on customer experience will unlock new opportunities and ease while resolving customer FAQs with a quick turnaround.

We understand the importance of a streamlined testing strategy for the success of the chatbot software application. Despite that, the mere presence of a testing strategy does not ensure the quality and performance of a specific group or product. The strategic selection of target groups or domains, and the right choice of testing methodologies, will go a long way in delivering the desired results.

Combined with testing best practices and industry standards, Indium has been helping clients overcome the obstacles of chatbot testing, helping expedite the development of remarkable chatbot apps that drive new business opportunities.

The post The Rise of The Chatbot: Opening New Vistas for Businesses  appeared first on Indium.

The way we work and live has been constantly changing in the last few years. Google CEO Sundar Pichai predicts that the advancement in artificial intelligence and machine learning will be even more revolutionary than the invention of fire.

According to Comptia, 86% of CEOs report that AI is considered mainstream technology in their offices as of 2021. Businesses across the globe are battling labour shortages, economic crises, and many other hurdles that affect business efficiency. Intelligent and comprehensive digital solutions include the use of artificial intelligence and machine learning as they are referred to as the ‘brains’ of smart machines that will help businesses deliver increased business productivity & constructive solutions. Many predictions in the field of artificial intelligence and machine learning are being made that we will see below:

Predictions about AI/ML in Business

• Accessibility and Democratization of Processes: Artificial intelligence and machine learning are no longer the responsibility of a single employee in the IT department. It is available to engineers, support representatives, sales engineers, and other professionals that can make use of it to solve everyday business problems. Machine learning will soon emerge to be the standard tool that is used to solve certain complex computational problems. It will help in personalizing customer experiences and provide an enhanced insight into customer behaviours.
• Enhanced Security for Data Access: AI & ML tools can track and analyze higher network traffics and recognize threat patterns to prevent cyber-attacks. This can be done in conjunction with monitoring the networks in question, detecting malware activities, and other related practices. Enterprises can adopt advanced AI solutions to both monitor data and construct special security mechanisms in their AI models. AI can help by recognizing patterns and suggesting business intentions using smart algorithms. AI-powered security will reach new heights in the days to come.
• Deep Learning to Aid Data Analysis: Deep learning happens after the creation of multiple layers of artificial neural networks to use for processing large amounts of unstructured data. This allows the machine to learn how to analyze and categorize inputs without being specifically instructed on how to handle the task. The use cases for deep learning range from industries such as predictive maintenance to product strategies in software development companies. Some autonomous locomotive and automobile enterprises are already implementing deep learning capabilities into their products. In the future, businesses across industries will increasingly leverage deep learning for data analysis.
• Natural Language Processing Enhancing Use Cases: Natural Language Processing involves both computational linguistics, and the general model of the human knowledge- paired with machine learning, statistical learning, and deep learning models all working closely with each other. NLP can help in making one aware of the subconscious patterns in the organization’s processes- this can help identify strategies to boost business efficiency. It is used both in the legal and commercial space, as dense legal contracts and documents and can be analyzed with speed.

Having got an insight into the probable trends for Artificial intelligence and machine learning, here we discuss a few use cases that are driving the use of AI/ML forward:

Use Cases for AI/ML in 2022

• Machine Learning in Finance: Machine learning techniques are paramount to enhancing the security of transactions by detecting patterns and possibilities of fraud in advance. Credit card fraud detection is an example of improving transactional and financial security through machine learning. These solutions work in real-time to constantly ensure security and generate alerts. Organizations across the globe use machine learning techniques to conduct sentiment analysis for stock market price predictions. In this instance, business trading is aided by the algorithm, where various data sources such as social media data help to perform sentiment analysis.
• Machine Learning in Marketing: Machine learning can aid with considering customer and business objectives while considering purchase patterns, pricing, comparison with other businesses, and mapping marketing points that can align with customer objectives. Content curation and development is an essential component in an era of digital marketing. There are tools that can help to customize the content as per the customer’s preferences and also tools that can help effectively organize content for customers for better engagement. Customization, understanding customers, and creating a memorable experience are all aided by machine learning as seen in the examples of chatbots that use AI technologies.
• Machine Learning in Healthcare: Administrative tasks can be delegated to natural language processing software, which can effectively reduce the physician’s and other healthcare staff’s overall workload. This can help the healthcare staff concentrate better on the patient’s health and spend less time going through legal and manual administrative work. NLP tools can help generate electronic health records and with managing critical administrative tasks in the healthcare industry. The tools would automatically find words and phrases to include in the electronic health record at the patient’s visit. They can create visual charts and graphs that can help the physician understand the patient’s health better.

Also Read: 10 Promising Enterprise AI Trends 2022

AI/ML Paving the Road Ahead for Growth

In 2022, along with the help of artificial intelligence and machine learning technologies, businesses will increasingly try to automate repetitive tasks and processes that involve sifting through large volumes of data and information. It is also possible that businesses will bring down their dependence on the human workforce to improve the overall accuracy, speed, and reliability of the information that is being processed.

AI/ML is usually called disruptive technologies as they are powerful enough to elevate industry practices by assisting organizations in achieving business objectives, making important decisions, and developing innovative services and products. Data specialists, analysts, CIOs, and CTOs alike should consider using these opportunities to efficiently scale their business capabilities to have an edge in the business.

The post AI & ML: Forecasts and Trends for 2022 and beyond appeared first on Indium.