Introduction
Let’s just say it out loud: data is messy. Perhaps in some utopia, it fits into well-organised spreadsheets and tables. In the real world, however, data is found in an uneven mix of structured and unstructured data, with the latter hiding the lion’s share of insights. Think audio (call recordings, audio notes, etc.), image (photographs, scans, etc.), text (emails, notes, messages, etc.), and video (CCTV, videos, meetings, etc.). What’s more, the volume of this data being generated is staggering.
The big-data revolution has amassed such vast amounts of data that we've barely scratched the surface in terms of its utilisation. There's an extensive reservoir of untapped insights waiting to be discovered. The potential for in-depth analysis is colossal.
Traditionally, working with large amounts of unstructured data (like images and video) required intricate algorithms and specialised software, making it a complex and time-consuming task. Its lack of a predefined format made it difficult to analyse with traditional data analysis methods.
On the other hand, while structured data was more organised and easier to query, it often required extensive data cleaning and preprocessing to handle missing values, outliers, and potential inaccuracies.
The emergence of Large Language Models has made it significantly easier to preprocess and analyse large volumes of this heterogeneous data. Because of the versatility and diversity of today’s data landscape, we need LLMs to be multi-modal.
What is Multi Modality?
At its core, multi-modality represents the convergence of diverse data types and forms into a singular, cohesive framework. This concept spans a broad spectrum, from numerical data and tables to text, voice recordings, images, videos, and even gestures. It's the idea of synthesizing varied inputs to create a richer, more comprehensive output.
Narrowing down to the realm of Large Language Models (LLMs), multi-modality takes on a transformative role. In this context, it allows LLMs to process, understand, and generate outputs based on a myriad of data types. Instead of being confined to just text, multi-modal LLMs can interpret images, analyze voice patterns, and even understand gestures, making them incredibly versatile and powerful tools in the tech landscape.
How does a multi-modal LLM help us?
The power of LLMs lies in their ability to learn from massive amounts of varied data. This property allows them to grasp the nuances of language and context.
Large Language Models (LLMs) combine text, images, and audio processing seamlessly, offering a wide range of benefits across industries. This breakthrough enhances communication by better understanding context, enabling the creation of diverse multimedia content, and improving image and video analysis. It also promotes accessibility and deepens data interpretation. We can also see how combining different types of data - text, voice, video, images, and tabular data - can lead to improved and more efficient decision-making in various industries.
Multi-modal LLMs unlock new possibilities for businesses and act as a major catalyst for AI-driven innovation. These models will fundamentally reshape how we engage with and leverage data in diverse realms.
Here is where products like Akaike’s BYOB become helpful. They allow you to harness the full potential of a Large Language Model (LLM) trained explicitly to your domain. This contextualisation empowers the model to provide highly accurate and contextually relevant responses, making it an invaluable asset for addressing a wide array of specialised tasks with precision and efficiency.
BYOB Description
What is BYOB?
Build Your Own Brain (BYOB), pioneered by Akaike Technologies, is an advanced system that leverages the power of generative AI to transform raw data into meaningful insights. It can understand and analyse data, converse with humans, and support decision-making and execution.
Unlike traditional analytics tools, BYOB is designed to handle multi-modality, meaning it can analyse various types of data - video, audio, text, or databases. BYOB connects with your data and helps you see hidden patterns, gain insights, and power up your business.
Let's explore how versatile a multi-modal LLM product like BYOB is and how it can be leveraged across various departments and industries.
(You will notice that an underlying theme for most industry data is- while it is available in large amounts in both structured and unstructured form- it is underutilised and siloed. BYOB can potentially solve for that. It will not just process the data and integrate it for use, but also tell you how it can be used.)
DEPARTMENT
INDUSTRY
Human Resources
Data Landscape: The Human Resources sector is a repository of very diverse data. By definition, we are looking at metrics that aren’t easily quantifiable. This encompasses structured formats like performance metrics and payroll records, as well as unstructured types such as video interviews, employee feedback, and email communications.
Specific Challenges in Data Utilisation: HR professionals often face the challenge of holistically assessing candidates and employees due to the sheer variety of data. Integrating insights from both structured and unstructured data to make informed decisions about hiring, promotions, or training can be complex. Additionally, ensuring fairness and transparency in decision-making, given the vast data sources, remains a consistent hurdle.
Functionality of Multi-modal LLMs: Multi-modal Large Language Models (LLMs) are great at processing and analysing this varied data. When tailored to HR datasets, these LLMs can provide comprehensive insights about employee satisfaction and progress and other metrics. This will help enhance candidate assessments, facilitate effective employee engagement strategies, and ensuring data-driven decision-making.Furthermore, it will provide a data-driven explanation of the decision-making process, promoting transparency and fairness.
| USE CASE |
DESCRIPTION |
MODALITIES |
| Streamlined Hiring |
Efficiently assess thousands of job applications by analyzing video and text resumes for candidate fit. |
Text (resume, cover letter), Video (video resume) |
| Employee Onboarding |
Provide a comprehensive understanding of company policies and job roles through interactive training materials and video sessions. |
Text (training materials), Video (training sessions) |
| Performance Evaluation |
Objectively evaluate employee performance using annual performance reviews and metrics. |
Text (performance reviews), Tabular data (performance metrics) |
| Employee Engagement Analysis |
Measure employee satisfaction and engagement levels to enhance workplace culture by analyzing survey responses and engagement metrics. |
Text (survey responses), Tabular data (engagement metrics) |
| Talent Management |
Identify and develop high-potential employees for leadership roles using employee profiles and talent metrics. |
Text (employee profiles), Tabular data (talent metrics) |
Sales
Data Landscape: In the realm of sales, data is generated at every customer touchpoint. This includes structured data like sales figures and lead information, and unstructured data such as call recordings, customer feedback, and email interactions.
Specific Challenges in Data Utilisation: Sales professionals grapple with the task of effectively harnessing this data to guide their strategies. From lead generation to conversion, the challenge lies in correlating diverse data sources to gain a holistic view of the customer journey. This is crucial for optimising each stage of the sales funnel and ensuring a seamless transition for potential customers.
Functionality of Multi-modal LLMs: Multi-modal Large Language Models (LLMs) are equipped to process and analyse the multifaceted data in sales. When trained on sales-specific datasets, these LLMs can provide actionable insights at every stage of the sales funnel. This includes refining lead targeting, enhancing customer engagement, and optimizing conversion strategies, ensuring a streamlined and effective sales process.
| USE CASE |
DESCRIPTION |
MODALITIES |
| Lead Scoring |
Prioritize a large pool of leads for follow-ups by scoring them based on interactions and historical data. |
Voice (call recordings), Tabular data (lead information) |
| Sales Forecasting |
Set realistic sales targets for the next quarter by analyzing historical sales data to forecast future trends. |
Tabular data (sales figures, lead information) |
| Customer Segmentation |
Create targeted marketing campaigns by segmenting customers based on their profiles and feedback. |
Tabular data (customer profiles), Text (customer feedback) |
| Sales Training |
Prepare the sales team for a new product line launch with enhanced training using interactive materials and video sessions. |
Text (training materials), Video (training sessions) |
| Competitive Analysis |
Understand the company's market position compared to competitors by analyzing market reports and sales data. |
Text (market reports), Tabular data (sales figures) |
Healthcare
Data Landscape: The healthcare sector is a rich source of varied data. Alongside structured elements like patient records and lab results, there's a vast amount of unstructured data- medical images, radiology scans, doctor's notes, and more.
Specific Challenges in Data Utilisation: Despite the wealth of information, the industry faces hurdles. The diverse and voluminous nature of the data often complicates early symptom detection, both in diagnostic and preventative realms. This can lead to missed opportunities for timely interventions.
Functionality of Multi-modal LLMs: Multi-modal Large Language Models (LLMs) offer a functional solution. When trained on healthcare data, these models can adeptly discern intricate health patterns, improving diagnostic accuracy and enabling more informed preventative care strategies.
| USE CASE |
DESCRIPTION |
MODALITIES |
| Preventative Diagnostics |
Harness medical records and lab results to identify health risks early, ensuring timely interventions. |
Text (medical records), Tabular data (lab results) |
| Treatment Plan Optimization |
Optimize treatment plans with historical data for superior patient outcomes. |
Text (medical records), Tabular data (treatment plans) |
| Medical Research |
Stand at the forefront of innovation by deriving insights from vast research data, shaping healthcare's future. |
Text (research papers), Tabular data (research data) |
| Patient Engagement |
Boost patient satisfaction by analyzing feedback and appointment data, enhancing healthcare delivery. |
Text (patient feedback), Tabular data (appointment data) |
| Resource Allocation |
Swiftly allocate resources during crises by analyzing availability and needs, ensuring optimal patient care. |
Tabular data (resource availability), Text (hospital needs) |
E-commerce
Data Landscape: E-commerce platforms are data-rich environments. They generate structured data such as sales metrics, inventory levels, and customer demographics, as well as unstructured data like product reviews, customer queries, and product images.
Specific Challenges in Data Utilisation: The game here is Optimisation Optimisation Optimisation. For e-commerce businesses, the challenge lies in synthesising this vast and varied data to enhance user experience. From understanding customer preferences to optimising inventory management, there's a need to integrate insights from both structured and unstructured sources to drive sales and ensure customer satisfaction. Data often remains underutilised due to the inability to correlate insights across different data modalities. This leads to less-than-optimal customer experiences and operational inefficiencies.
Functionality of Multi-modal LLMs: Multi-modal Large Language Models (LLMs) are tailored to handle the intricacies of e-commerce data. When trained on e-commerce datasets, these LLMs can offer insights that optimise product recommendations, streamline inventory management, and enhance customer support, ensuring a seamless shopping experience for users.
| USE CASE |
DESCRIPTION |
MODALITIES |
| Customer Insights |
Improve customer satisfaction by analyzing interactions to identify common pain points and sentiments. |
Voice (call recordings), Text (written feedback) |
| Inventory Management |
Optimize inventory to prevent overstocking or stockouts and streamline the supply chain. |
Tabular data (inventory levels), PDFs (shipping documents) |
| Product Recommendation |
Enhance user experience by recommending products through textual and visual analysis of product data. |
Text (product descriptions), Image (product images) |
| Customer Support Automation |
Improve response time by automating routine customer inquiries through analysis of textual and voice interactions. |
Text (customer inquiries), Voice (customer calls) |
| Sales Trend Analysis |
Plan for future inventory needs by analyzing sales figures and market reports to identify sales trends. |
Tabular data (sales figures), Text (market reports) |
Finance
Data Landscape: The finance sector juggles a mix of data types. This includes structured formats like transaction records and financial statements, alongside unstructured data such as market news, analyst reports, and client communications.
Specific Challenges in Data Utilisation: Navigating the finance world requires sifting through vast data to make informed decisions. The challenge? Correlating diverse data sources to spot market trends, assess risks, and predict future financial shifts. Moreover, the rapid pace of the financial world demands real-time data analysis for timely decision-making.
Functionality of Multi-modal LLMs: Multi-modal Large Language Models (LLMs) are primed for the financial data maze. When trained on finance-specific datasets, these LLMs can pinpoint market sentiments, analyze transaction patterns, and forecast financial trends, aiding in more precise and timely financial strategies.
| USE CASE |
DESCRIPTION |
MODALITIES |
| Risk Assessment |
Banks assess tranche risks using credit data and loan terms. |
Tabular (credit ratings), PDFs (loan agreements) |
| Market Sentiment Analysis |
Traders gauge sentiment from textual content and financial news videos. |
Text (news articles), Video (news segments) |
| Fraud Detection |
Financial institutions detect fraud by monitoring transactions and textual communication. |
Tabular (transactions), Text (emails, chats) |
| Portfolio Optimization |
Investment managers refine portfolios using investment data and market news. |
Tabular (investment records), Text (market news) |
| Credit Scoring |
Lenders generate credit scores by evaluating financial and employment data of applicants. |
Tabular (credit history), Text (employment records) |
Legal
Data Landscape: When we think law, we think mountains of paperwork. The Legal sector is a labyrinth, teeming with data - from structured case histories and legal precedents to the more unstructured realms of court proceedings, whether they be in audio or video, and the myriad of client communications.
Challenges in Data Utilization: The sheer depth and breadth of this data present a formidable challenge. Sifting through, correlating, and deriving insights from both structured and unstructured data can be a herculean task. This often translates to extended case timelines and strategies that might not fully harness the available information.
Functionality of Multi-modal LLMs: Enter the capabilities of multi-modal Large Language Models (LLMs). These models, when attuned to the nuances of legal data, can revolutionise the way legal professionals operate. From dissecting legal documents to in-depth case reviews and litigation support, they promise a more navigable and enriched understanding, poised to significantly elevate the efficiency and efficacy of legal processes.
| USE CASE |
DESCRIPTION |
MODALITIES |
| Case Document Analysis |
Analyze case documents to extract relevant information and precedents. |
Text (case documents), PDFs (legal filings) |
| Audio/Video Analysis for Court Proceedings |
Transcribe and analyze audio or video recordings of court proceedings to extract relevant information. |
Audio (court recordings), Video (court proceedings) |
| Client Communication Analysis |
Analyze client communications to understand their needs and concerns better. |
Text (emails, letters), Voice (phone calls) |
| Legal Research |
Automate the process of legal research by analyzing legal documents and case histories. |
Text (legal documents, case histories) |
| Contract Review and Analysis |
Analyze contracts to ensure compliance and identify potential risks. |
Text (contracts), PDFs (signed agreements) |
Manufacturing
Data Landscape: The manufacturing sector is a veritable treasure trove of data. Alongside structured components like machine logs and production schedules, there's a vast array of unstructured data, encompassing maintenance reports and product images pivotal for quality assurance.
Challenges in Data Utilization: The crux of the matter is the evident underutilization of this data. This oversight often culminates in operational hiccups and a lag in addressing pressing issues, hindering optimal production flow.
Functionality of Multi-modal LLMs: This is where multi-modal Large Language Models (LLMs) come into play. These models, when fine-tuned to the manufacturing milieu, can weave together disparate data strands, offering a cohesive operational view. Imagine correlating machine logs with maintenance narratives to preemptively flag maintenance needs, ensuring product quality, and fine-tuning production workflows. It's about harnessing the latent potential of previously overlooked data, guiding not just in comprehension but also in actionable insights
| USE CASE |
DESCRIPTION |
MODALITIES |
| Quality Control |
Factories detect quality inconsistencies using machine outputs and product scans. |
Sensor Data (outputs), Image (scans) |
| Maintenance Prediction |
Plant managers predict machine maintenance needs using health metrics and past logs. |
Sensor Data (health metrics), Tabular (maintenance logs) |
| Supply Chain Optimization |
Manufacturers refine supply chains using inventory data and shipping documents. |
Tabular (inventory, supplier records), Text (documents) |
| Production Forecasting |
Factories set production rates based on past rates and market forecasts. |
Tabular (past rates, sales figures), Text (forecasts) |
| Resource Allocation |
Production managers allocate resources efficiently using availability data and task requirements. |
Tabular (availability, requirements), Text (plans) |
Marketing
Data Landscape: Marketing is all about understanding users and their behaviour. This means any data we get about users gives us insight and we need to use. Yes, it is campaign metrics and customer demographics, but it is also customer feedback, social media content. It is all about understanding how the your users respond to the content you are putting out.
Challenges in Data Utilisation: Despite the richness of this data, there's a recurring challenge: effectively harnessing it to craft resonant messages and strategies. The diverse nature of the data often makes it difficult to glean a holistic understanding of customer behaviour, preferences, and the evolving market landscape.
Functionality of Multi-modal LLMs: Imagine understanding feedback data in real-time, and proactively changing strategy to better target your audience. Or being able to customise content to a more specific audience segment.
This is where multi-modal Large Language Models (LLMs) shine. When attuned to the nuances of marketing data, these models can weave together insights from varied data sources. This enables marketers to craft more targeted campaigns, understand real-time audience sentiment, and predict emerging market trends, ensuring that every marketing move is data-informed and strategically sound.
| USE CASE |
DESCRIPTION |
MODALITIES |
| Content Recommendation |
Analyze user reviews and multimedia content to curate tailored content for different audience segments. |
Text (user reviews), Multimedia (video and audio content) |
| Audience Sentiment Analysis |
Gauge public sentiment after a product launch by processing social media posts and user-generated content. |
Text (social media posts, feedback), Image (user-generated content) |
| Campaign Optimization |
Refine an ongoing campaign based on its performance metrics and feedback to suggest optimal adjustments. |
Tabular Data (campaign metrics), Text (ad copies, feedback) |
| Brand Perception |
Analyze text and video content to understand the brand's public perception and image. |
Text (news articles, blogs), Video (promotions, ads) |
| Influencer Collaboration |
Evaluate influencer posts and their audience feedback to recommend potential influencer collaborations for a new campaign. |
Text (influencer posts, audience feedback), Image (influencer content) |
Education
Data Landscape: The educational sector is replete with a myriad of data types. This encompasses structured data like student performance metrics and curriculum modules, alongside unstructured data such as classroom discussions, feedback, and multimedia educational content.
Challenges in Data Utilisation: The traditional educational model, with its broad-brush approach, often struggles to effectively utilize this diverse data. The result? A system that doesn't always cater to individual learning needs, leaving some students feeling underserved, especially those who deviate from the 'standard' learning trajectory.
Functionality of Multi-modal LLMs: Multi-modal Large Language Models (LLMs) offer a promising solution. When attuned to educational data, these models can analyze and correlate varied data points, enabling the creation of tailored learning experiences. This means lessons adapted to individual learning styles, pacing, and needs, fostering a more inclusive and effective educational environment.
| USE CASE |
DESCRIPTION |
MODALITIES |
| Personalized Learning |
Provide tailored learning experiences for students based on their individual performance and preferences. |
Text (course materials), Tabular data (performance metrics) |
| Curriculum Development |
Update and enhance the school curriculum using educational resources and feedback from students. |
Text (educational resources), Tabular data (student feedback) |
| Online Classroom Management |
Effectively manage online classes by analyzing class recordings and interactions between students and teachers. |
Video (class recordings), Text (student interactions) |
| Student Performance Analysis |
Evaluate student performance by analyzing their grades and feedback from teachers to identify areas for improvement. |
Tabular data (grades), Text (teacher feedback) |
| Educational Resource Optimization |
Optimize the allocation and utilization of educational resources across various departments based on usage metrics. |
Text (educational resources), Tabular data (usage metrics) |
Agriculture
Data Landscape: Agriculture is a sector deeply rooted in data. This encompasses structured information like soil quality metrics, crop yield data, and weather forecasts, as well as unstructured data such as satellite imagery, farmer anecdotes, and pest activity reports.
Challenges in Data Utilisation: Despite the wealth of data available, the agricultural sector often faces hurdles in effectively synthesising and acting upon this information. The inability to correlate diverse data sources can lead to suboptimal farming practices, misaligned resource allocation, and missed opportunities for yield optimisation.
Functionality of Multi-modal LLMs: This is where multi-modal Large Language Models (LLMs) come into the picture. When tailored to agricultural data, these models can seamlessly integrate varied data sources, providing actionable insights. From predicting optimal planting seasons based on weather patterns to identifying potential pest infestations through image analysis, LLMs can significantly enhance decision-making and operational efficiency in agriculture.
| USE CASE |
DESCRIPTION |
MODALITIES |
| Crop Yield Prediction |
Analyze soil health data and satellite imagery to forecast crop yields, aiding in planning and sales. |
Sensor Data (soil health), Image (satellite imagery) |
| Pest Detection |
Process drone footage and pest reports to detect infested areas early, guiding timely interventions. |
Image (drone footage of fields), Text (pest reports) |
| Irrigation Optimization |
Evaluate soil moisture data and rainfall forecasts to determine optimal irrigation schedules for efficient water usage. |
Sensor Data (soil moisture), Weather Data (rainfall forecasts) |
| Supply Chain Management |
Streamline the agricultural supply chain by analyzing harvest records and market trends. |
Tabular Data (harvest records, demand forecasts), Text (market trends) |
| Sustainable Farming Practices |
Recommend sustainable and efficient farming techniques based on research articles and crop health data. |
Text (research articles, farmer feedback), Sensor Data (crop health) |
Government and Public Sector
Data Landscape: The government and public sectors are vast repositories of data. Data that seemingly dates back decades and decades. This includes structured datasets like census records, budget allocations, and policy documents, as well as unstructured data forms such as public feedback, video recordings of public addresses, and inter-departmental communications.
Challenges in Data Utilisation: Given the sheer volume and diversity of data, the government often grapples with challenges in data integration, transparency, and timely decision-making. The inability to efficiently correlate and act upon diverse data sources can lead to policy misalignments, resource misallocations, and gaps in public service delivery.
Functionality of Multi-modal LLMs: Multi-modal Large Language Models (LLMs) can be instrumental in this context. When trained on government and public sector data, these models can provide a holistic view of diverse datasets, enabling more informed policy-making, efficient resource allocation, and enhanced public service delivery. By synthesising varied data points, LLMs can aid in crafting strategies that resonate more closely with public needs and sectoral objectives.
| USE CASE |
DESCRIPTION |
MODALITIES |
| Policy Analysis |
Assess the impact of a newly implemented policy by correlating policy documents and public feedback with policy outcomes. |
Text (policy documents, public feedback), Tabular Data (policy outcomes, metrics) |
| Fraud Detection |
Monitor transaction data and reports to identify fraudulent activities in grant allocations. |
Tabular Data (financial transactions, grant allocations), Text (reports, complaints) |
| Public Service Optimization |
Improve public transportation by analyzing service usage stats and user feedback to identify areas of improvement. |
Tabular Data (service usage stats), Text (public feedback, service reviews) |
| Budget Allocation |
Allocate the annual budget efficiently by reviewing past budgets and current demands. |
Tabular Data (previous budgets, expenditure), Text (department requests, public demands) |
| Citizen Engagement Analysis |
Understand citizen sentiment towards a new initiative by processing feedback from various sources. |
Text (public forums, feedback), Video (public addresses, meetings) |
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