Author Archives: Jim Griffin

Jim Griffin
As the Director of the Analytics Practice at Robosoft, Jim Griffin brings a wealth of experience in analytics, machine learning, CRM, and loyalty. Over a career spanning 20 years, he has experience in predictive models, customer lifetime value, marketing mix modelling and more, across continents. An MBA in Marketing from the University of Minnesota, he also serves as faculty at The University of Texas at Austin - McCombs School of Business.
Artificial Intelligence

Why the Google Gemini Launch Matters

On December 7, Google announced the launch of Gemini, its highly anticipated new multi-modal AI architecture, including a Nano version optimized for hand-held devices. The announcement was greeted with mixed reviews.

Some users expressed doubts about the claims made by Google or whether the Gemini product was significantly better than GPT-4. Quoting an AI scientist who goes simply by the name “Milind,” Marketing Interactive suggested that Google is playing catch up at this point and that OpenAI and Microsoft might be ahead by six months to a year in bringing their AI models to market.

There was also plenty of public handwringing about a promotional video by Google featuring a blue rubber duck because the demo had been professionally edited after it was recorded.

Despite the tempest in a teapot about the little blue rubber duck, we believe the announcement is essential and deserves our full attention.

Decoding Gemini: How Parameters Shape Its Capabilities

Parameters are, roughly speaking, an index to how capable an AI might be. GPT 4.0 was built on 1.75 trillion parameters.

We don’t know how many parameters were used to build Gemini. Still, Ray Fernandez at Technopedia estimated that Google used between 30 and 65 trillion parameters to make Gemini, which, according to SemiAnalysis, would equate to an architecture that might be between 5 and 20x more potent than GPT-4.

Beyond the model’s power, there are at least four points of differentiation for Gemini.

#1. Multi-modal Architecture: Gemini uses multi-modal architecture from the ground up, unlike the competing architectures, which have text, images, video, and code in separate silos, which forces other companies to roll out those capabilities one by one, complicating the ability for them to work together in an optimum way.

#2. Massive Multitask Language Understanding: Gemini scored higher than its competition on 30 out of 32 third-party benchmarks. On some of those, they were only slightly ahead, and on others, more, but overall, that’s an imposing win-loss record.

In particular, Gemini recorded an essential milestone by outscoring human experts on a tough test called Massive Multitask Language Understanding (MMLU). Gemini scored 90.04% versus a human expert performance, which scored 89.8%, according to the benchmark authors.

#3. Alpha Code2 Capabilities: Simultaneously with the launch of Gemini, Google also launched Alpha Code2, a new, more advanced coding capability that now ranks within the top 15% of entrants on the Codeforces competitive programming platform. That ranking represents a significant improvement over its state-of-the-art predecessor, which previously ranked in the top 50% on that platform.

#4. Nano LLM model: Also simultaneous with the launch of Gemini was the Nano LLM model, which is optimized to run on a handheld device, bringing many of Gemini’s capabilities to edge devices like handheld phones and wearables. For now, that’s a unique advantage for Gemini.

points of differentiation for Google Gemini

What are the practical implications of Gemini Nano on a handheld device?

Companies like Robosoft Technologies that build apps will collaborate with clients to test the boundaries of what Nano can do for end users using edge devices like cell phones.

Edge computing emphasizes processing data closer to where it is generated, reducing latency and dependence on centralized servers, and cell phones will undoubtedly be first in line to benefit from Nano because they can perform tasks like image recognition, voice processing, and various types of computations on the device itself.

What about Wearables or other Types of Edge Devices?

Google hasn’t said whether Nano can run on wearables or other edge devices, but its design and capabilities suggest it probably can.

First, Nano is a significantly slimmed-down version of the full Gemini AI model, making it resource-efficient and potentially suitable for devices with limited computational power, like wearables.

Also, Nano is designed explicitly for on-device tasks. It doesn’t require constant Internet connectivity, making it ideal for applications where data privacy and offline functionality are crucial — both are relevant for wearables.

In particular, we noticed that Google’s December 2023 “feature drop” for Pixel 8 Pro showcased a couple of on-device features powered by Nano, including “Summarize” in the Recorder app and “Smart Reply” in Gboard. In our opinion, these capabilities could easily translate to wearables.

What about Apple Technology?

There’s no official indication that Nano is compatible with Apple technology. We think such compatibility is unlikely because Google primarily focuses on Android and its ecosystem.

However, the future of AI development is increasingly open-source and collaborative, so it’s possible that partnerships or independent efforts by members of the AI ecosystem — including companies like Robosoft Technologies — could lead to compatibility between Gemini Nano and Apple devices.

Enterprise-Level Use Cases for Gemini Pro

From what we know so far, Gemini Pro offers good potential to enable or enhance various enterprise-level applications. Here are some critical use cases that we think are most likely to be among the first wave of projects using Gemini Pro.

Customer Service and Workflows

  • Dynamically updating answers to FAQs
  • Helping with troubleshooting
  • Routing questions to the appropriate resources
  • Extracting and summarizing information from documents, forms, and datasets
  • Filling in templates
  • Maintaining databases
  • Generating routine reports

Personalization and Recommendations

  • Creating personalized marketing messages and recommendations
  • Optimizing pricing
  • Automating risk assessments
  • Streamlining loan applications
  • Providing personalized health treatment plans
  • Recommending preventive health measures

Business Process Optimization

  • Identifying process delays
  • Optimizing resource allocation
  • Streamlining decision-making processes with improved information flow
  • Identify cost savings opportunities

Security and Fraud Detection

  • Identifying potential cyber-attacks
  • Identifying malicious code and protecting sensitive data
  • Analyzing financial data for suspicious activity to help prevent losses

Content Moderation and Safety

  • Moderating user comments and posts on social media, including forum discussions
  • Improving the correct identification of spam

Above all, a very foundational use for Google Gemini Pro might be to enable the implementation of an enterprise-level generative AI copilot.

What is an Enterprise-Level Generative AI Copilot?

A generative AI copilot is an advanced artificial intelligence system designed to collaboratively assist and augment human users in various tasks, leveraging productive capabilities to contribute actively to the creative and decision-making processes. This type of technology is customized for specific enterprise applications, learning from user interactions and context to provide tailored support. It goes beyond conventional AI assistants by actively generating real-time suggestions, solutions, or content. It fosters a symbiotic relationship with users to enhance productivity, creativity, and problem-solving within organizational workflows.

Why might Gemini Pro be a good platform for building a generative AI copilot?

We think that Gemini Pro should be considered a possible platform for building a copilot. Its capabilities and characteristics align well with the requirements of such a system.

First, Gemini Pro can process and generate human language effectively, enabling it to understand user intent and respond coherently and informally. It has a knowledge base built on 40 trillion tokens, equivalent to having access to millions of books. It can reason about information, allowing it to provide relevant and insightful assistance to users.

Also, like other generative AI platforms, Gemini Pro can adapt its responses and behavior based on the context of a conversation, helping to ensure that its assistance remains relevant and helpful.

So that’s a good foundation.

Upon such a foundation, Google relies on the partners in its ecosystem to build an overall solution that addresses enterprise needs. These include ensuring that their data is secure. That information inside their enterprise is not used to train public models, control access to the data based on job roles and other factors, help with data integration, and build an excellent user interface. These are examples of areas where technology partners like Robosoft Technologies make all the difference when bringing an AI-based solution to life within an enterprise.

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Digital Transformation Tech Talk Technology

Data Visualization and Digital Transformation: A Powerful Partnership

Data visualization is the process of translating data into a visual format, such as a chart or graph, to make it easier to understand and interpret. It is the key to unlocking the insights hidden in your data. It is a powerful tool that can be used to communicate insights, identify trends, and make informed decisions.

Data visualization is becoming increasingly important in today’s data-driven world. Companies of all sizes use it to improve their operations, make better business decisions, and communicate their findings to stakeholders. However, data visualization can be complex to implement, and many companies face challenges such as a lack of expertise, limited resources, data silos, and compliance concerns.

A digital transformation partner can help companies overcome these challenges and get the most out of data visualization. With their expertise, experience, and resources, digital transformation partners can help companies develop and implement data visualization solutions tailored to their needs.

How to Get Value from Data Visualization: A Step-by-Step Guide

Data visualization is a powerful tool for communicating insights and driving action. However, simply creating charts and graphs is not enough. To get the most value from data visualization, it is essential to take a thoughtful approach that considers the goals of the visualization, the audience, the type of data, and the design principles involved.

Here is a step-by-step guide to help you get value from data visualization:

  • Define your objectives: What do you want to achieve with your data visualization? Do you want to inform a decision, drive action, or communicate a complex idea? Once you know your objectives, you can choose the right visualization type and design approach.
  • Know your audience: Who will be viewing your data visualization? Tailor the visualization to their knowledge and interests. Consider their level of expertise in the subject matter and preferred style of consuming information.
  • Choose the right visualization type: Many data visualizations are available, each with strengths and weaknesses. Choose a visualization type that effectively represents your data and aligns with your objectives. Some common types include bar charts, line charts, pie charts, scatter plots, and heat maps.
  • Simplify and focus: Keep your visualization simple and clutter-free. Remove any unnecessary elements that do not contribute to the message. Focus on highlighting the most critical insights or trends within the data.
  • Use appropriate scales: Ensure that the rankings and axes in your visualization are relevant and do not distort the data. Use linear or logarithmic scales as needed. Label axes, including units of measurement, to aid interpretation.
  • Emphasize storytelling: Craft a narrative around your data visualization. Explain the context, background, and significance of the data. Guide your audience through the story your data tells using annotations, captions, and headings.
  • Interactivity: Consider adding interactive visual elements, such as tooltips, filters, or drill-down options. Interactivity can engage your audience and allow them to explore the data independently.
  • Data integrity and accuracy: Ensure your data is accurate and current. Any errors or inaccuracies can lead to misleading conclusions or a lack of confidence in the tool. Cite your data sources and provide transparency about any data preprocessing or transformations.
  • Design aesthetics: Consider design principles like color choice, font selection, and visual consistency. Use color purposefully to draw attention to essential elements or to represent categorical data. Today’s audiences typically react better to flat design principles.
  • Test and iterate: Once you have created a draft of your data visualization, test it with a sample of your target audience to gather feedback. Continuously refine and improve your visualization based on user feedback and changing requirements.
  • Mobile and accessibility: Ensure that your visualization is accessible to a wide range of users, including those who are color blind in some spectrums. Optimize for mobile devices, as many people access data on smartphones and tablets.
  • Measure impact: After deploying your data visualization, measure its effects on decision-making or understanding. Analyze user engagement and gather feedback to assess whether your objectives were met.

By following these steps, you can create data visualizations that are informative, engaging, and effective. This approach is valuable because it helps to transform raw data into meaningful insights that can inform decisions, drive action, and communicate complex information effectively. Effective data visualization can make data more accessible, engaging, and memorable, leading to better-informed decisions and improved communication of insights to a diverse audience.

Benefits of Informative, Engaging, and Effective Data Visualization

Critical Attributes of a Data Visualization Tool

When choosing a data visualization tool, it is essential to consider the following critical attributes:

  • Ease of use: The tool should have an intuitive interface that is easy to learn and use, even for users with limited technical expertise.
  • Data integration: The tool should be able to import and connect to various data sources, including spreadsheets, databases, cloud services, and APIs.
  • Visualization types: The tool should offer various visualization types, including bar charts, line charts, pie charts, scatter plots, heat maps, and maps.
  • Customization options: The tool should allow you to customize the appearance of your visualizations, including colors, fonts, labels, and interactive elements.
  • Interactivity: The tool should support interactive features, such as tooltips, drill-down options, filters, and hover effects, to enhance user engagement and exploration.
  • Performance: The tool should be able to handle large datasets and complex visualizations without significant lag or slowdowns.
  • Export and sharing: The tool should allow you to export visualizations in various formats, such as images, PDFs, and interactive web formats, and easily share them with others.
  • Collaboration: If you need to collaborate on data visualization projects, consider tools that offer real-time collaboration features and version control.
  • Integration with other tools: Check if the tool integrates with other software you use, such as data analysis tools, business intelligence platforms, or reporting software.
  • Automation and templates: Some tools offer automation features and templates to streamline the creation of standard visualizations and reports.
  • Data security: Ensure the tool adheres to security and compliance standards, especially when working with sensitive or regulated data.
  • Cost and licensing: Consider the pricing model of the tool and the total cost of ownership.


Leading Data Visualization Tools

Optimizing Data Visualization: Key Errors to Avoid

The most significant mistake data visualization users make is misrepresenting or misinterpreting data due to poor design choices or a lack of critical thinking. This can lead to misleading or inaccurate conclusions and poor decision-making.

Some common errors and mistakes include:

  • Misleading visuals: They can include using inappropriate scales, truncated axes, or omitting data points.
  • Overcomplicating visuals: Trying to convey too much information in a single visualization can overwhelm the audience and make it difficult to discern the key insights.
  • Lack of context: Failing to provide sufficient context or background information to help the audience understand the data’s significance or the visualization’s purpose.
  • Ignoring data quality: Using incomplete, inaccurate, or improperly cleaned data can lead to incorrect conclusions and decisions.
  • Choosing the wrong visualization type: Select a visualization type that doesn’t align with the data’s characteristics or the message you want to convey. For example, using a pie chart to show time trends.
  • Ineffective use of color: Poor color choices can confuse or mislead viewers. Using too many colors, inconsistent color schemes, or failing to distinguish between categorical and sequential data can be problematic.
  • Omitting labels and legends: Missing or incomplete axis labels are often a key source of confusion. Data legends are also important. Be sure to include information about the represented start and end time period.
  • Not testing with the audience: Failing to gather feedback from the intended audience or stakeholders can result in visualizations that don’t effectively communicate the desired message.
  • Overloading with data: Visualizing massive datasets without proper aggregation or summarization can result in cluttered and unreadable visuals.
  • Copying reports without adapting: Simply replicating the visualization format of previous information on new data without understanding the appropriateness for that unique situation or audience.
  • Failure to update: Not updating visualizations with new data or changes in the underlying dataset can lead to outdated and potentially misleading information.

To avoid these mistakes, data visualization users should:

  • Prioritize data accuracy: Ensure the data is clean, complete, and current before creating any visualizations.
  • Maintain a critical mindset: Be aware of the potential for bias and deception in data visualization. Question your assumptions and challenge the status quo.
  • Seek feedback: Get feedback from the intended audience or stakeholders before sharing your visualizations.
  • Invest time learning best practices in data visualization: Many resources are available to help you create compelling and informative data visualizations.

Effective data visualization is more than just creating appealing graphics; it’s about accurately communicating meaningful insights from data. By avoiding the mistakes listed above and following the tips provided, you can create data visualizations that are informative, engaging, and trustworthy.

How can a Digital Transformation Partner Help Enhance Data Visualization?

A full-service digital transformation partner can add significant value to a data visualization tool by:

  • Customizing data visualizations: Developing custom data visualization components tailored to the specific needs and requirements of the company’s clients. This may involve creating unique chart types, dashboards, or data representation techniques not readily available in off-the-shelf tools.
  • Integrating with existing applications: Integrating the data visualization tool seamlessly into the company’s software applications can help ensure a unified and streamlined experience, allowing users to better visualize and analyze data within the context of their workflow.
  • Enabling real-time data visualization: Enabling real-time data streaming and visualization capabilities. This is valuable for applications that require monitoring and immediate insights into changing data, such as IoT (Internet of Things) applications or financial trading platforms.
  • Expanding data source connectivity: Expanding the visualization tool’s capabilities by enabling connections to various data sources, including databases, APIs, cloud storage, and external data providers. This flexibility allows users to work with diverse datasets.
  • Incorporating advanced analytics: Incorporating advanced analytics and machine learning capabilities into the data visualization tool. This can empower users to perform predictive analysis, anomaly detection, clustering, or other data-driven tasks directly within the application.
  • Developing custom reporting features: Developing custom reporting features that allow users to generate and export reports based on visualized data. This is valuable for business intelligence and data-driven decision-making.
  • Implementing robust security and access control: Security features like user authentication, profile-based access levels, and data encryption can help protect sensitive information.
  • Optimizing for scalability and performance: Optimizing the data visualization tool for scalability and performance to handle large datasets and a growing user base without compromising speed or responsiveness.
  • Ensuring cross-platform compatibility: Ensuring that the tool is compatible with various operating systems and devices, including web browsers, mobile devices, and desktop applications, to maximize accessibility.
  • Applying design thinking: Ensuring the overall experience meets users’ needs.
  • Gathering feedback and iterating: Continuously gathering user feedback to identify areas for improvement and refine the data visualization tool’s features and usability.
  • Ensuring compliance with data privacy and regulatory requirements: Ensure that interactive user features such as drill-down capabilities do not inadvertently expose sensitive customer data in ways that were not intended.

digital transformation partner help transform data visualization


Adding these value-added features and capabilities to a data visualization tool, a full-service digital transformation company can add considerable value and provide clients with more powerful tools for extracting insights from their data. This can increase customer satisfaction and create a competitive edge in industries that rely on data-driven decision-making.

data visualization and digital transformation a powerful partnership

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