Imagine tea producers walking into a tea grading facility and seeking assurance of consistent quality and precision in their blends. As they assess the brews, they rely on the distinct aroma, the perfect balance of flavors, and the exquisite quality that sets each tea apart. But how do they ensure such consistency? The fusion of traditional expertise and cutting-edge technology holds the secret. Machine learning has emerged as a powerful tool in the world of tea grading, revolutionizing the way tea is assessed and appreciated. Let’s embark on a journey to explore the incredible potential of machine learning in elevating tea grading to new heights. 

The Steeped Challenges of Traditional Grading 

Before we plunge into the realm of machine learning, let’s steep ourselves in the challenges faced by traditional tea grading methods. Firstly, relying solely on human tasters can lead to inconsistencies and subjective interpretations of tea attributes. It’s like having a group of friends with different taste preferences arguing over the perfect cup of tea! Secondly, the process can be time-consuming and requires a substantial number of skilled tasters, making it difficult to meet the demands of large-scale tea production. Lastly, maintaining consistent quality standards over time becomes quite the balancing act, just like finding the perfect harmony between tea and milk. 

Infusing Machine Learning into the Mix 

Here comes the exciting part! Machine learning algorithms to the rescue! By harnessing the power of data and automation, we can create a more objective and efficient grading system.  

Picture this: the dance of algorithms, sifting through countless data points, uncovering patterns, and learning to grade tea with the precision of a master taster. It’s like having a virtual tea expert by your side, helping you find the perfect cuppa every time. 

The Technical Steeping of Tea Grading with Machine Learning 

Let’s take a closer look at the technical solution architecture that makes this tea grading transformation possible. At the heart of the system lies a robust framework built with Python, leveraging powerful libraries like scikit-learn, TensorFlow, and PyTorch. These libraries provide the building blocks for developing and training machine learning models. 

The architecture incorporates both current and historic data. Current data includes attributes like leaf size, color, aroma intensity, and batch details. Historic data captures past grading records, weather conditions, and other relevant factors. This comprehensive dataset serves as the foundation for training our machine learning model. 

Using Python code, the data is pre-processed and transformed to ensure compatibility with the chosen machine learning algorithms. Dimensionality reduction techniques, such as Principal Component Analysis (PCA), may be employed to extract the most relevant features from the data, further enhancing the model’s performance. 

Now, let’s introduce the star of the show: the Predictor! This component takes in new tea samples, analyzes their attributes using computer vision techniques, and feeds them into the trained machine learning model. The model, like a knowledgeable tea taster, predicts the grade of the tea based on the learned patterns. 

Predicting the Validity of Tea Grades 

One intriguing aspect of using machine learning in tea grading is the ability to predict the validity of tea grades over time. By formulating this problem as a regression task, we can estimate the duration after which a tea grade becomes invalid. The input data for this prediction includes sample tea information, catalog data, batch dates, sample dates, tasting dates, and grading dates. 

By training regression models and assessing their performance using metrics like Root Mean Squared Error (RMSE), we can provide tea enthusiasts with valuable insights into the lifespan of tea grades. This information empowers individuals to make informed decisions about the freshness and quality of their tea purchases. 

Sustainability of Tea Grades: Predicting the Perfect Sip 

Tea grades, like the delicate flavors they embody, have a limited shelf life. To ensure tea is savored at its best, predicting the duration of a grade’s validity becomes crucial. Using regression techniques, factors like sample tea information, catalog data, batch dates, and tasting dates are considered to estimate the duration after which a grade becomes invalid. This prediction helps tea enthusiasts make informed decisions about the freshness and quality of their favorite blends. 

A Sip into the Future: Brewing Innovation 

As we pour ourselves a cup of innovation, let’s savor the benefits of integrating machine learning into the tea grading process. Firstly, it elevates the accuracy and consistency of grading, ensuring you always experience the flavors you desire. Secondly, it reduces dependency on human tasters, making the process more efficient and cost-effective. Lastly, it empowers tea producers to monitor and analyze the attributes of their tea in real time, allowing them to maintain the highest standards of quality. 

By embracing these remarkable innovations, we unlock a world where tea enthusiasts can confidently embark on a captivating exploration of diverse tea varieties, reassured by the transformative influence of machine learning on the grading process. Now, as you read this, you might be inspired to adopt this cutting-edge technology and revolutionize your tea grading practices. We extend an open invitation for you to connect with us, enabling a seamless transition into a realm where machine learning empowers your tea grading endeavors. 

Imagine the possibilities: with our expertise and guidance, you can seamlessly integrate machine learning into your tea grading process, enhancing accuracy, efficiency, and overall satisfaction. We provide the tools, knowledge, and support necessary for you to confidently navigate this new frontier of tea appreciation.  

Moreover, the techniques and principles we employ in tea grading can be extended to other flavor and fragrance-centric analyses. Imagine applying similar methodologies to wine grading, perfume mixing, and more. The possibilities are endless, and we are excited to explore these avenues in the future. 

Reach out to us today and discover how this remarkable technology can transform your tea experience, allowing you to savor the intricate flavors and aromas with newfound clarity and confidence. Let’s embark on this journey together and unlock the full potential of machine learning in the world of sensory analysis. 

In the rapidly evolving landscape of technology, Artificial Intelligence (AI) has emerged as a game-changer across various industries. The pharmaceutical sector, too, has embraced this revolutionary technology to enhance its business operations and drive innovation. With the market for AI in pharma projected to experience exponential growth in the coming years, organizations need to understand the profound impact AI can have on their industry. In this blog, we will delve into the top four use cases of AI in the pharmaceutical sector, highlighting the benefits it offers to companies operating in this domain.

Use Case 1: AI in Drug Discovery

One of the most significant ways AI is transforming the pharmaceutical industry is through its impact on the drug discovery process. By leveraging advanced algorithms and biochemistry knowledge, AI is revolutionizing the way new drugs are discovered. Here are some key benefits of using AI in drug discovery:

Unbiased Approach: AI models adopt an objective and unbiased approach to drug discovery by not relying on predetermined targets. This allows for a more comprehensive exploration of potential drug candidates.

Time and Resource Savings: AI enables virtual drug screening, drastically reducing the time and resources required for identifying promising drug candidates. This efficiency can accelerate the entire drug discovery process.

Personalized Treatment Options: AI-powered computer vision models can accurately analyze patient reports, assisting physicians in creating personalized treatment options. This capability has the potential to significantly improve patient care and outcomes.

Case Study: AstraZeneca, a leading pharmaceutical company, has successfully utilized AI and Machine Learning (ML) to elevate its drug discovery process. By streamlining the identification of potential drug targets and optimizing the development process, AstraZeneca has harnessed the power of AI to drive innovation in the pharmaceutical industry.

Use Case 2: Computer Vision for Drug Manufacturing

AI-based computer vision systems have found extensive applications in drug manufacturing, particularly in quality assurance and error prevention. The advantages of employing AI in this context include:

Efficient Quality Control: Computer vision-enabled systems can swiftly and accurately examine drugs on conveyor belts, promptly detecting any defects or anomalies in shape, color, and packaging. This capability ensures that only high-quality products reach the market.

Contamination Prevention: By reducing human touchpoints in the manufacturing process, AI minimizes the risk of contaminations, thus enhancing product safety. Pharmaceutical companies can rely on AI to maintain stringent quality standards.

Case Study: DevisionX has developed an AI-powered computer vision system capable of detecting defective medicines on conveyor belts. By ensuring high-quality production, this technology significantly contributes to improving drug manufacturing processes.

Use Case 3: Predictive Forecasting

AI plays a pivotal role in predicting pandemics, seasonal illnesses, and other healthcare trends. In the pharmaceutical industry, accurate predictive forecasting enables companies to optimize their supply chains, resulting in improved operational efficiency. Here are some key benefits of using AI in predictive forecasting:

Improved Supply Chain Planning: AI-powered predictive models help pharmaceutical companies prepare for demand fluctuations and match supply with demand effectively. By accurately forecasting future requirements, companies can streamline their operations and avoid shortages or excess inventory.

Case Study: Emory University and Google have employed AI to predict sepsis outbreaks. This proactive approach allows healthcare providers to allocate resources more efficiently, ultimately leading to improved patient care.

Use Case 4: AI in Clinical Trials for Drugs

AI has brought significant advancements to clinical trials, revolutionizing various stages of the process. The contributions of AI in this domain include:

Candidate Recruitment: By analyzing historical records, diseases, and demographic data, AI can identify suitable candidates for drug trials, enhancing trial efficiency and reducing recruitment challenges.

Trial Design: AI leverages vast amounts of data from previous trials to extract meaningful insights, aiding in the design of effective clinical trials. This data-driven approach increases the chances of successful outcomes.

Trial Monitoring: By combining AI with IoT-enabled wearable devices, real-time monitoring of patients during treatment becomes possible. This provides valuable insights into the effectiveness of treatments, allowing for timely adjustments if necessary.

Now, let’s explore how leading pharmaceutical companies are leveraging AI to drive innovation and generate increased Return on Investment (RoI).

 

Johnson & Johnson: Pioneering AI in Pharma

Johnson & Johnson (J&J), a prominent pharmaceutical company, has been at the forefront of AI implementation since 2015. The collaboration between J&J and IBM’s Watson Health has allowed J&J to harness the power of AI for processing vast amounts of healthcare data and providing evidence-based responses in natural language to professionals.

a. Robotic Surgery: J&J established Verb Surgical, a joint venture with Google Verily, to develop AI and Machine Learning (ML)-powered surgical robots. These robots are designed for performing minimally invasive medical surgeries, revolutionizing surgical procedures.

b. AI in Drug Discovery & Development: J&J capitalizes on AI to remain competitive in drug discovery, design, and development. By combining their expertise with intelligent AI strategies, J&J actively works towards creating new drugs, treatments, and surgical methods. Precision medicine is a focus area for J&J, aiming to provide personalized healthcare services based on patients’ genetic profiles, leading to improved patient outcomes and reduced healthcare costs.

c. AI for Diagnosing Diseases & Drugs: J&J explores the application of AI in diagnosing diseases and predicting drug responses. By utilizing platforms such as WinterLight Labs, J&J can monitor neuropsychological details to detect and understand Alzheimer’s disease. WinterLight’s AI platform analyzes speech-based data, facilitating the automatic analysis of Alzheimer’s.

Bayer AG: Harnessing AI in Cardiovascular and Oncology Drug Discovery

Bayer AG, a global pharmaceutical company, has embarked on a collaboration with Exscientia, a leading AI-driven drug discovery company. This partnership focuses on utilizing AI to accelerate the discovery of small molecule drugs targeting cardiovascular disease and oncology.

a. AI-powered Drug Discovery: Exscientia’s Centaur Chemist™ platform, powered by AI algorithms, plays a pivotal role in automating and optimizing the design of novel drug candidates. By combining evolutionary computing and deep learning techniques, the platform enhances productivity and efficiency in the drug discovery process.

b. Targeted Projects: Under the collaboration agreement, Bayer and Exscientia are working on specific projects with predetermined targets in cardiovascular and oncology therapeutics. AI enables the precise identification of suitable drug targets and lead structures, expediting the drug discovery timeline.

c. Potential Benefits: The collaboration aims to achieve project milestones earlier, reducing the time and resources required for identifying and optimizing potential drug candidates. This improvement in efficiency leads to overall productivity enhancement.

d. Financial Agreement: As part of the agreement, Exscientia may receive up to €240 million, including upfront and research payments, milestones, and potential sales royalties. Bayer’s commitment to this financial agreement demonstrates their recognition of the value and potential impact of AI-driven drug discovery.

e. Advancing Digital Transformation: Bayer’s collaboration with Exscientia highlights their commitment to digital transformation in research and development. By leveraging the power of AI, Bayer aims to simplify and accelerate the drug discovery process, ultimately improving patient outcomes and addressing critical healthcare needs.

Roche Holding AG: Embracing AI Investment

Roche Holding AG, one of the largest global pharmaceutical and diagnostic companies, has actively leveraged AI technology to enhance its drug development operations. The acquisition of Flatiron Health in 2018 strengthened Roche’s capabilities in maintaining vast amounts of oncology data, leading to more accurate diagnoses and treatment plans through machine learning systems.

Additionally, Roche collaborated with IBM and Sensyne Health for predictive analytics projects related to diabetic retinopathy and chronic kidney diseases, respectively. These partnerships demonstrate Roche’s commitment to harnessing AI’s potential in predicting disease outcomes and enhancing clinical trials.

Pfizer: Harnessing AI for Advanced Healthcare Solutions

Pfizer, one of the largest multinational drug development organizations in the United States, has partnered with IBM to accelerate the adoption of AI technology. Leveraging IBM Watson, a cloud-based platform, Pfizer utilizes vast amounts of medical data to enhance early cancer detection and discover innovative therapies.

By harnessing the power of AI, Pfizer aims to revolutionize healthcare through:

Advanced Data Analysis: Pfizer utilizes AI algorithms to analyze millions of medical data points, including patient records, diagnostic images, and genomic information. Processing this wealth of data enables the identification of patterns and indicators contributing to early cancer detection. This facilitates timely interventions and the development of targeted treatment strategies.

Novel Therapy Discovery: AI-driven analysis helps Pfizer identify potential breakthrough therapies for various cancers. By mining extensive medical data, Pfizer aims to uncover new treatment targets and innovative approaches to combat the disease. This accelerates the development of novel therapies that have the potential to improve patient outcomes and prolong lives.

In conclusion, the pharmaceutical industry is witnessing the transformative power of AI across various domains. From drug discovery and manufacturing to predictive forecasting and clinical trials, AI is revolutionizing the way pharmaceutical companies operate. Leading companies like Johnson & Johnson, Bayer AG, Roche Holding AG, and Pfizer are actively leveraging AI to drive innovation, enhance research and development processes, and ultimately improve patient outcomes. By embracing AI, these companies are at the forefront of the industry, bringing cutting-edge solutions to the healthcare landscape.

Experience the transformative power of AI in the pharmaceutical industry with Navikenz. Partner with us to unlock the full potential of your data, accelerate innovation, and transform patient care. Join us in shaping the future of medicine with AI-driven excellence.

Retail therapy is one of the best mood enhancers for me. Two of my favorite brands to shop from are Zara and Marks & Spencer. The assisted shopping experience I get in these stores is very good. While I try to search for a particular size or color of a garment, a Zara sales store manager would check on his phone if it is available in his store or any other store in the city, and if they can courier it to me if not available in their store. As a consumer, I find this to be a great experience. However, shopping behavior has permanently changed over the years, especially in the post-COVID era. As the world grapples with the impact of the COVID-19 pandemic, consumer behavior has shifted drastically from offline to online channels. Traditional retailers are now faced with the challenge of meeting evolving customer expectations in this rapidly changing landscape. As per a Mckinsey report on tech transformation in retail, in Germany alone, online sales experienced a staggering annual growth rate of 23.0 percent from 2019 to 2020, while offline sales only saw a modest increase of 3.6 percent each year. Retailers need to set a North Star to guide their aspirations for customer experience. In-person store engagement has shifted to online engagement, and that’s where Conversational AI is the new perfect shopping assistant. So, what is conversational AI?

Conversational AI refers to the use of artificial intelligence-powered virtual assistants, chatbots, and voice assistants to facilitate natural language interactions with customers. These intelligent systems are capable of understanding and responding to customer queries, providing personalized recommendations, and even processing transactions, all in a conversational manner. Now that we know what conversational AI is, let’s try to understand why it has become a nearly perfect shopping assistant.

24/7 Accessible

In the new digital world, geography is no longer a limitation for retailers. Retailers need to have the ability to field customer queries across time zones 24/7 and act upon the queries instantly. That’s where Conversational AI chatbots come into play. These online shopping bots are around-the-clock self-service tools, allowing customers to reach out to retailers and resolve their queries anytime and anywhere. Chatbots for the retail industry enable a smooth conversational flow during the customer journey all the time, without having to wait for an agent to respond or be restricted by “working hours.” Retail chatbots are not only capable of serving 24/7 but are also significantly cheaper than onboarding more agents with rotational shifts.

Time & Money Saver

Implementing a conversational AI chatbot can quickly help with common tasks such as ticket labeling, routing, and answering frequently asked questions. Automating ticket routing can be especially helpful in avoiding delays for support teams. With the help of AI, companies can train models to label and route customer inquiries based on past data, freeing up valuable time for agents to focus on higher-level customer issues. If the customer support query is complex or beyond the scope of the retail chatbot, there is a seamless process to hand off the query to a live agent based on their skill sets and current workload. This enables a smooth, hassle-free customer experience for the support teams in the retail industry.

When support teams are equipped with effective AI tools, they feel empowered to provide better customer service, resulting in high levels of customer satisfaction and a positive customer experience. Additionally, this creates a positive work environment for support agents who feel supported and valued in their day-to-day activities.

Improves In-Store Experience

Conversational AI can automate in-store operations and reduce a substantial amount of operational expenses in retail stores. It can help sales personnel assist customers in the store, reduce queues through contactless payment methods, replenish stock by real-time stock monitoring, and overall improve the in-store experience for customers.

Personalized Customer Experience & Making Informed Business Decisions Based on Data

Conversational AI is also capable of detecting the mood, intent, and interest of your customers throughout the purchase journey. Some global retail brands have even introduced a facial recognition system for this function installed at checkout lanes. If a customer is annoyed, a store representative will immediately talk to him or her. Retail chatbots also leverage the intent prediction feature to understand customers’ tone, context, and behavior. It helps retailers build stronger relationships with customers by providing personalized assistance throughout the conversational flow. With AI, retailers can also predict customer choices by analyzing various data points such as demographics, location, social media comments, and reviews. This personalized approach to retail shopping can help increase both online and offline sales and improve the overall customer experience.

Kmart Australia, for example, has developed an AI-powered digital assistant called Kbot that integrates with the augmented reality (AR) functionality on its website. It lets customers interact with products such as furniture and see what they will look like in their homes. Once they have found a product they’re interested in, they can use voice to ask questions about the product, such as where it’s in stock and when it can be delivered.

Also, with all the data being collected around customers, businesses can gain insights into customer needs and identify areas of improvement, which further helps in supporting businesses to make informed decisions.

Now, let’s move to how retail businesses are leveraging Conversational AI.

Companies leverage conversational AI in retail in several ways to enhance customer experiences and drive business growth. Here are some common ways companies utilize conversational AI:

We recognize the profound impact conversational AI is having on the retail landscape and stand ready to assist retailers in harnessing the power of conversational AI, enabling them to thrive in a dynamic and customer-centric market.

Ready to enhance your retail business with Conversational AI? Contact us now to explore how our AI-powered solutions can revolutionize your customer experience and drive business growth. Don’t miss out on the opportunity to thrive in the dynamic and customer-centric market. Get in touch with us today!

Language models have been leading the way in advancing natural language processing, allowing for the comprehension and generation of text that closely resembles human language. However, recent progress has broadened their ability to also handle structured data. In this blog post, we will delve into the ways in which language models can be utilized to process and analyse structured data, presenting intriguing opportunities for various practical applications.

Structured data encompasses organized information presented in a predetermined format, such as spreadsheets, databases, or tables. It consists of distinct fields, records, and connections between various entities. In contrast to unstructured data, which comprises free-form text, structured data possesses a predefined schema, enabling straightforward interpretation and analysis using conventional approaches. Applying a language model to structured data necessitates comprehending both the data itself and its underlying schema.

An enduring challenge in the Data & AI field has been for business users to acquire understandable information in a readily comprehensible format from structured data. The initial hurdle lies in structuring the data according to a business domain schema, which is the primary step in transforming data into valuable insights. Subsequently, defining relationships and granularity becomes crucial to ensure that all potential queries are accommodated within the domain models. Unfortunately, this process has historically constrained the freedom of business users to query the data according to their needs, regardless of how the underlying business model was constructed.

The greatest advantage for business users in employing Large Language Models (LLMs) is the unrestricted ability to compose queries. To showcase this potential, we have developed a demonstration utilizing a basic table. Our confidence in the applicability of LLMs extends beyond this example, as we envision their utilization in other domains such as data validation and quality assessment. Through LLM-driven insights, business users can also gain access to validation adherence, further enhancing their decision-making capabilities.

The utilization of a relational database for LLM model consumption can be outlined through the following steps:

  1. Identify the pertinent data tables: Determine the tables within the existing relational database that hold the data required for LLM analysis.
  2. Extract the data: Retrieve the data from the identified tables by executing SQL queries.
  3. Perform LLM analysis: Utilize a supported Open AI library for LLM analysis to examine the data obtained from the selected table, identifying patterns and relationships.

Driven by our unwavering commitment to continuous innovation, we have developed a demonstration showcasing the effectiveness of LLM models in handling structured data. This breakthrough offers exciting possibilities for business users to directly interact with structured data, freeing them from the limitations imposed by pre-determined business models based on query patterns. It empowers users to explore the data directly and leverage its potential without being bound by predefined constraints.

Given below is a structured table containing comprehensive employee information.

Here are a few examples of responses generated using LLM models:

In conclusion, LLMs provide a means for natural interaction with structured data. Rather than relying on conventional query languages, we can engage with data directly through conversational means. This simplifies the process of interaction and promotes data democratization. LLMs also contribute to the identification of data patterns and anomalies, facilitating exploratory data analysis. Ultimately, LLMs enhance the accessibility and interactivity with data, bridging the gap between users and the wealth of information contained within the data.