Genomics is the field of biology that studies an organism's complete set of genetic material (DNA), including all of its genes and non-coding sequences. With the rapid development of AI/Machine learning techniques, there has been a surge in the application of machine learning in genomic research.

AI and ML have great potential as the world evolves around genomics. Some of the critical areas include but are not limited to.

1. Variant analysis
2. Drug Discovery
3. Personalized medicine

Variant analysis:

With the advent of data available, machine learning can identify genetic variants that are associated with specific diseases.

Machine learning can help researchers and development teams develop new diagnostic tests and treatments for those diseases.

Drug discovery:

Geometric drug discovery involves using computational methods to predict the structure and interactions of drug compounds with target proteins.

Machine learning is used to identify new drug candidates and to predict the toxicity of potential drug compounds, reducing the time and costs associated with drug development.

Additionally, machine learning can help design protein-based drugs by predicting their three-dimensional structure and identifying targetable regions, accelerating drug development for various diseases.

Personalized medicine:

Personalized genomics is the study of an individual’s genetic makeup, and machine learning can play a crucial role in predicting disease risk, personalizing treatment plans, and interpreting genetic data.

By analyzing large datasets, machine learning algorithms can identify patterns associated with developing certain diseases and predict drug responses or side effects based on genetic markers.

So obtained info can be used to provide personalized risk assessments and develop targeted prevention and early detection strategies. Additionally, machine learning can simplify the complex process of interpreting genetic data and provide customized recommendations.

Genome Editing and CRISPR-Cas9

CRISPR-Cas9 (Clustered Regularly Interspaced Short Palindromic Repeats)gene editing technology has benefited from machine learning techniques.

Ml methods like ANN or SVM, to name a few, help in predicting the efficiency and specificity of the CRISPR-Cas9 guide RNA designs. Machine Learning can also aid in enhancing the safety and efficacy of this powerful tool.

Challenges:

The applications of AI/ML in genomics have both potential advantages and, at the same time, have a few key challenges that need to be addressed and to name few:

1. Data Quality and Quantity:
   ML algorithms are data-dependent and have high volumes of high-quality data.
   Obtaining the same would be difficult because of the variations in data formats and limited sample sizes.
2. Ethical and Privacy Concerns:
   Genomic data contains sensitive and personalized data that concerns patient privacy and security.
   Safeguarding patient privacy while enabling data sharing for research purposes is a complex challenge that needs to be addressed through robust data protection measures.
3. Bias and fairness:
   In genomics, where disparities related to race, ethnicity, and gender exist, biased AI/ML models can amplify these biases.
4. Generalization and reproducibility:
   Models developed for genomics often need help with generalization beyond the datasets they were trained on. Genomic data, generally, are more heterogenous, which the models fail to capture.

Conclusion:

As machine learning continues to evolve, it promises to revolutionize genomics research, leading to groundbreaking discoveries and transforming the future of healthcare. According to a report, The global AI in the genomics market would reach up to $9.8 Billion, Globally by 2031 at 40.6% CAGR. This clearly states a significant market growth in genomic research and clinical applications.

Thanks for reading!…

Watch this space for more additions to the list of topics. Feel free to shoot me any questions in the comments below or connect with me on LinkedIn.

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Unlocking the Potential of machine learning for genomics research was originally published in DataDreamers on Medium, where people are continuing the conversation by highlighting and responding to this story.

People are all aware of the history of chatgpt, how it all started and why it started, and how it came to the state it is today, with a new version of GPT-4 coming this Wednesday. I want to jot down my understanding and predictions for the future in this article.

A brief understanding of GPT

Chatgpt is a language model based on a neural network architecture called a transformer. Since their first appearance as transformers in a research paper, they have been widely used in building language models.

At a very high wage level, the sole purpose of chatgpt is to predict the next word in a sequence of text; this is done by training the model on a large corpus of data (Current ChatGpt3 is trained on 175 billion parameters).

One key feature of ChatGpt, which everyone knows, is its ability to generate coherent and semantically meaningful text, even when the user input is incomplete or impartial. This is achieved using the (attention mechanism), allowing the model to break the entire input text and give more weight to particular words or phrases more relevant to the required prompt.

ChatGPT 4 New Features:

1. Outperforms exisitng LLMs, State of the art models
2. Outperforming GPT 3.5 in exam results
3. Visual Inputs
4. Steerability

Limitations

1. Hallucinating facts
2. Truthful QA
3. Bias
4. Lack of events on events occured after Sep 2021
5. Sensitive prompts further needs improved

Source: https://openai.com/research/gpt-4

So What next …..

As everyone is talking about the current tech Generative AI, it can be seen as both an aid and a bane to human consumption.

When we see from the angle of the aids, we know a lot of creative and innovative use cases and startups coming to light that integrated ChatGpt into their existing products as well as new products. These expand over diverse industries, including commerce, healthcare, and People Analytics, and aid people working in tasks like L1 ticket support, Marketing team, and Content creators, to articulate just a few.

So as an extension, Microsoft also released the Visual ChatGPT, which acts as an interface between ChatGPT and visual feature models (VFMs). The prompt manager helps ChatGPT determine whether it needs to use a VFM to provide the necessary output and takes care of iterative reasoning and housekeeping. The immediate manager is critical to the system as it is responsible for non-language queries and allows ChatGPT to rely on the capabilities of VFMs rather than hallucinations. Visual ChatGPT lowers the hurdle to access text-to-image models and potentially incorporates compatibility across various AI tools.

While there are strict assumptions about how the models can be easily misused, there is a high chance these models can be more biased based on the data on which there are trained; these can easily create offensive outputs.

My Views.

With the new Gpt4, the Hallucinations problem will continue escalating, and it might be challenging to handle the bias. And As evident, it does an excellent job of giving the output with its model being trained on higher data points than GPT3. Also, ethical problem handling might be complex cause handling these models at a large scale still needs time and effort.

Nevertheless, as time advances, we will see much development coming into the light. Even OpenAi opened Eval capabilities to know the developer perspectives and are looking for more contributions from developers.

Thanks for reading!…

Watch this space for more additions to the list of topics. Feel free to shoot me for any discussions in the comments below or connect with me on LinkedIn.

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The Future of Conversational AI: ChatGPT4 Takes Center Stage again was originally published in DataDreamers on Medium, where people are continuing the conversation by highlighting and responding to this story.

In this article, I want to express my thoughts on how the field has evolved and what holds up in it.

1. Voice or Face Based Automation

Voice- and face-based automation in commerce offers a unique opportunity to personalize the shopping experience for customers and increase overall sales for retailers.

Both methods use voice or facial recognition to identify the customers, analyze their preferences and purchase history, and make product recommendations based on that information. These methods also have great potential to improve the customer experience and increase customer loyalty.

Credits: Intel-powered Outdu

Users can interact with self-service terminals and kiosks without touching the screens for navigation, and inputs are accepted only after a face is detected wearing a mask for enhanced security.

Simple, intuitive hand gestures perform scrolling, moving, selecting, and canceling. Audio keywords are used to navigate, and speech is used to enter specific data in alphabets and numbers.

Lee’s Famous Recipe Chicken Restaurant is using a new conversational artificial intelligence (AI) solution to increase the speed of service and shorten wait times in their drive-thrus. It is empowered by Hi Auto which answers drive thru customers queries and confirms the order after selecting relavant options in menu.

2. RFIDs

Radio-frequency identification (RFID) tags are tiny strips of metal that transmit radio waves with product information such as brand, price, size, color, inventory levels, and location in the store. RFID tags have replaced barcode labels and can accelerate the checkout process when scanned automatically.

RFID technology has also been used in stores to automate the sale of beverages; customers can take their preferred drinks, wave the item under a reader to get product details and pay by text message. Other retailers, like Amazon Go and JD.Com, have developed “Just Walk Out” and line-free technology that uses sensors and computer vision to track consumer activity and automatically scan and charge items to their accounts.

In addition, RFID tags can interact with customers as every object in a store can interact with them. Retailers can point a reader at a product or display rack to provide information about the product range, brand, price, availability, and other features.

Many retailers have introduced interactive fitting rooms where customers can try on items virtually. Japanese retail giant Uniqlo has introduced a virtual fitting room where customers can stand in front of an AI-enabled mirror and see an image of themselves wearing the product. TriMirror, a Canadian technology company, has also introduced 3-D interactive avatars to help customers check for fit in different garment locations and customize the item as desired.

The innovation of lightweight RFIDs has transformed how we use these devices, as they can be disposable on consumer products. Compared with Barcode scanners, these devices can store more metadata information and be used for more than the checkout process. Walmart acquired Zeekit, a leading dynamic virtual fitting room platform.

Virtual fitting room market forecast to experience a compounding growth of $14.87 billion by 2029 from $4 Billion in 2022.

The recent advancement in cross-border Payment between the governments also better aids another funnel.

3. AI-powered recommendations and Virtual agents

Retail experiences can be curated, advised, and recommended to customers where interacting with customers has become challenging due to the after-effects of a pandemic.

Many startups use AI to develop intelligent conversational chatbots to understand customer shopping patterns. These virtual agents guide the shoppers from discovery to checkout across buying platforms like apps, sites, Instagram, and WhatsApp, which helps sales. Most common customer queries can also be routed to these virtual agents for automated answering.

They generate the store customers from leads to buyers by nurturing their patterns using the contact information. Target the high-value/intent customers by providing personalized product deals and building customers' confidence in the conversion of purchases.

A conversion rate optimization tool known as “Nudge” has a proven track record of increasing conversion rates by up to 12% and average order value by up to 25%. It’s easily customizable and appears as a fully native value-added benefit on your website, driving shoppers to complete purchases and increasing the average order value per conversion.

Recent disruptions in the AI industry, like ChatGPT, will make this segment more personalized by bringing in the external trends and conversations even better shortly.

4. Digital recreation and sensory information

Companies like Gastrograph AI build and train the world’s largest sensory database by digitally measuring the flavors. They are also helping CPG companies create breakthrough products or optimize existing ones for specific markets worldwide.

AI’s self-learning system can determine which flavor and preference patterns work best in each place.

“We have modeled over 1,000 flavor signatures to date (and counting) that are easy to interpret by formulators,” its website states. Artificial intelligence can “lift and shift” your data and identify small, impactful changes to optimize your product for an untapped market. Avoid complete product reformulation, and access new markets with confidence that your product will succeed.

Similarly, there are advances in digital recreations of the products through holographs which can be used for buyer interaction without needing a VR headset. These ideas can also be leveraged for buyer interaction and improve customer purchase and retention rates when customers need clarification about which products to choose.

Summary:

As discussed, the rise of contactless commerce and how technology transforms our shop. It explores four primary areas of innovation, including voice and face-based automation, RFID technology, AI-powered recommendations and virtual agents, and digital recreation and sensory information.

The article highlights how these technologies are helping retailers personalize the shopping experience for customers, increase sales, and improve customer loyalty. The pandemic has accelerated the adoption of contactless commerce, and the article predicts that technology will continue to play a significant role in the future of retail.

Thanks for reading!…

Watch this space for more additions to the list of topics. Feel free to shoot me any questions in the comments below or connect with me on LinkedIn.

If you thought this was interesting, leave a clap or two and subscribe for future updates.

The Future is Here: The Rise of Contactless Commerce was originally published in DataDreamers on Medium, where people are continuing the conversation by highlighting and responding to this story.

Building on the ideas presented in our previous article, I delve deeper into the subject to provide a more comprehensive understanding.

Target Audience?

• Beginners of data science who want to shift into the sales and marketing domain.
• Who wants to implement new ideas in sales and marketing domains

Data science can be used in various sales and marketing applications, such as:

1. Customer segmentation: using demographics, behavior, and purchase history to divide customers into groups for targeted marketing
2. Predictive modeling: using past customer data to predict future sales and inform marketing strategies
3. Personalization: using data to create personalized experiences for customers, such as product or content recommendations
4. Marketing attribution: using data to track the effectiveness of different marketing channels and campaigns
5. Lead scoring: using data to prioritize information and identify the most promising sales prospects.
6. Customer lifetime value prediction: using historical data of customer behavior and demographics to predict how much a customer will spend in the future with a company
7. A/B testing: using data to test different versions of marketing campaigns and website designs to see which performs better.

A few scopes and examples are discussed below.

1. Prospect Modeling — Predictive Modeling

Problem:

Let’s assume nearly X million users have registered with the customer e-commerce platform; however, they have never made a single purchase. The marketing team has a challenge identifying which of these million user bases are the perfect candidates for better targeting to increase activations.

Solution:

Based on the browsing patterns of the new and existing users, we developed a machine learning approach that builds a look-alike model and predicts which prospects among the list are highly likely to convert.

Approach :

1. Identify the traits of existing High-Value customers by going back in time and exploring their features before users convert into customers.
2. Typically, High-Value customers tend to convert with specific days of activations.
3. Observe the browse patterns for the first six days of incoming prospects and futurize their behavior

Prospect modeling can also be combined with other techniques, such as market segmentation, customer lifetime value prediction, and marketing attribution, to understand a business’s potential customer base better.

2. Churn Analytics — Customer Segmentation

Churn analytics in data science analyzes customer behavior and predicts which customers are likely to cancel or stop using a company’s products or services. This can be a valuable tool for businesses, allowing them to address customer issues and retain valuable customers proactively.

Problem:

Identifying the customers who are at risk of churning out/unsubscribing/ leaving the product

Solution:

Build unified metrics that showcase the health of customer experience and proactively identify accounts/products at risk of losing them

Approach:

1. Data Collection: Gathering data on customer demographics, behavior, and purchase history.
2. Data Cleaning: Cleaning and preparing the data for analysis by removing any missing or irrelevant data.
3. Exploratory Data Analysis (EDA): Analyzing the data to identify patterns and trends in customer behavior.
4. Churn prediction: Building a predictive model using machine learning techniques such as logistic regression, decision trees, or neural networks to predict which customers are likely to churn.
5. Model Evaluation: Evaluate the predictive model’s performance by measuring its accuracy, precision, and recall.
6. Model deployment: Deploy the model in production to predict real-time churn.
7. Churn prevention: Taking action based on the predictions to prevent customer churn, such as launching retention campaigns, providing incentives to stay, or addressing specific customer issues.
8. Post-deployment monitoring: Continuously monitoring the model’s performance and retraining the model if necessary.

Exclusive Tips:

1. As a starting step, categorize the high-level levers/KPIs segments like Demographics, Voice of customer, Engagement
2. Drill down into these categories and give a weightage to these levers
3. List down the possible data sources and techniques ( lift charts, IGs) to be used
4. Build a user-friendly score that is easily understandable to the sales team for rightly targeting the users

3. Account Potential Forecasting

Account potential forecasting uses data to predict the potential revenue a business can generate from a specific customer or group of customers (accounts). It is typically used by sales and marketing teams to prioritize their efforts and allocate resources effectively.

Overview of Account Potential Forecasting

The potential account value is a range based on the model output and can be calculated at any time.

The Potential Account Model does not account for absolute New Logo capture

Account potential would be the sum of the cumulative values from new product sales, renewal effects, and expense value.

4. Marketing/Channel Attribution — Using

Determine the best path for increasing user conversion for specific campaigns or platforms.

1. Last-click attribution: credits the last marketing touchpoint before conversion with the entire value of the transformation.
2. First-click attribution: credits the first touchpoint with the entire value of the conversion.
3. Linear attribution: assigns equal credit to each touchpoint in the conversion path.
4. Time decay attribution: assigns more credit to touchpoints that occurred closer in time to the conversion.
5. Position-based attribution: assigns a higher percentage of credit to the first and last touchpoints in the conversion path.
6. Algorithmic attribution: uses machine learning techniques to determine the optimal credit allocation to different touchpoints in the customer journey.
7. Attribution with data visualization: using data visualization techniques like heatmap and path analysis to identify the most critical touchpoints in the customer journey.
8. Multi-touch attribution uses statistical models to assign credit to all the touchpoints in the customer journey.

Conclusion

In conclusion, data science has revolutionized how companies approach sales and marketing. By leveraging data-driven insights, businesses can now make more informed decisions, target their audience more effectively, and measure the effectiveness of their campaigns. However, it is essential to note that data science is not a magic solution but a powerful tool that must be used in conjunction with other strategies and business acumen. As companies continue to collect and analyze more data, it will be crucial for them to stay up-to-date with the latest technologies and best practices in data science. By embracing data science, companies can gain a competitive edge and drive growth in the highly competitive world of sales and marketing.

Watch this space for more additions to the list of topics. Feel free to shoot me any questions in the comments below or connect with me on LinkedIn.

Thanks for reading!…

If you thought this was interesting, leave a clap or two and subscribe for future updates.

Instant Ideas in Data Science — Sales & Marketing — Part 2 was originally published in DataDreamers on Medium, where people are continuing the conversation by highlighting and responding to this story.

“Any sufficiently advanced technology is indistinguishable from advanced technology “ — Arthur C. Clarke.

edgw computing - Bing video

The present world that we live in. Need a solution where we are closely related to the world of computation, automation in collaboration with data storage!

Thanks to EDGE COMPUTING

The seeds for edge computing were laid back in the 1990s with the primary aim of serving the web and the video content from various edge servers! With the latest advances in networking ,Slowly the edge computing evolved and even got commercialized. For example, dealer locators, shopping carts, real-time data agitators in present real-world scenarios.

According to a great scientist Karim Arabi, Edge computing operates on instant data, i.e., the real-time data generated by the users or sensors, whereas cloud computing, on the other hand, depends on big data.

Concept:

With the increase in IoT devices, data is being generated at that massive rates. As a result, monitoring that Hercules amount of data at their data centers is difficult, Despite the improvements in the network hardware, there is no guarantee. Acceptable transfer rates and response times!

So, edge computing’s main task lies in moving away the computation from the data centers towards the network’s edge.

Privacy and security:

The distributed nature of edge computing will define a scope for change in the scheme of protection used in cloud computing. Different encryption mechanisms even have to be employed . A shift in the structure is noticeable. Which can even pave a path in transferring ownership to the end-users.

Speed and Efficiency:

Edge computing brings both analytical and computational resources close to the end-users and, therefore, speeds up communication speed.

This would be an added trait for some devices which need a short response time.

With the proximity of the analytical resources to the end-users, tools like sophisticated analytical and Artificial Intelligence can run on the system’s edge. This placement at the edge will increase operational efficiency and contribute many advantages to the system.

Applications:

Edge computing will reduce the volumes of data that must be moved, the consequent traffic, and the distance that data must travel.

An added advantage for facial recognition algorithms showed considerable improvements in response times, as demonstrated in early research.

Edge computing is a tremendous advantage for pixel Streaming

Smart cities, connected cars, Autonomous cars , industry 4.0, etc..