Xplore_AI: Want to avoid AI Whiplash? Don’t Chase.

by | May 21, 2025 | Nuix, Uncategorized, Xplore AI

In 1970, futurist Alvin Toffler published a book entitled “Future Shock”, which covers?the psychological distress and anxiety caused by rapid technological and societal change. Toffler’s feeling was that most of society’s problems are symptoms of ‘information overload’. 

 
Ironically, the problem is exacerbated by technology itself. Internet ‘cookies’ and AI algorithms track our digital behaviors to curate data to sling at our hungry eyeballs, which are glued to the ubiquitous screens and devices we carry with us everywhere. Navigating, processing, assimilating, and making sense of all this information is not only an exercise in futility, but it is also unnatural and unhealthy. 

Initially, I considered this issue to be merely an annoyance and something to kvetch about at parties. After all, we always have the option to ignore the noise and side-step the issue all together, right? Well, not really. That may work in our personal lives but in a professional context, many of us are under increasing pressure to innovate, drive better outcomes, and do more with less… yesterday! Like it or not, we need to pay attention to what’s happening. This is particularly true when it comes to keeping up to date on AI, where ignorance is not bliss and getting it wrong could have dire consequences. So… what can we do? 
 

Unfortunately, there is no easy “fix.”  However, one thing sprung to mind while watching a pro hockey game this week, my favorite sport. Don’t chase.  For those of you who have never played a sport, this may be a new concept, but it is simple. In sports like soccer, hockey, or basketball, when the coach yells “don’t chase” it means the players should avoid frantically pursuing the ball, puck, or their opponents in a disorganized manner. Instead, they should maintain their position, stay disciplined, and anticipate the play, allowing them to respond strategically rather than reactively. Get the picture? 
 

Applying this one simple tweak will not turn off the firehose of information hitting us daily.  But with some prep work and practice, it will help you remain “in the pocket” (calm under pressure) allowing you to mindfully select what you give your attention to. 
 

Here’s why it’s important and how it applies to both sports and your corresponding professional role: 

  1. Maintain Positioning: When players chase, or over-commit, they often leave their assigned positions, creating gaps and opportunities for the opposing team. Staying in position helps maintain the team’s structure and reduces vulnerabilities. In your job, this means to avoid getting sucked into every AI article, opinion, trend, opinion or webinar. Note that a pre-requisite here is having a documented AI strategy or at least a core set of priorities as a foundational structure. Without a plan, you are more prone to inefficient chasing because there is no baseline for the priorities. 
  2. Conserve Energy: Constantly chasing can lead to fatigue, which affects performance during a game. By playing smarter and picking their moments, players can conserve energy for when it is needed most. In our daily work, there are only so many hours in each day. Following an AI game plan lets you avoid wasting your energy, and maximizes your more precious resource, time. 
  3. Control the Pace: Rather than letting the opponent dictate the pace, not chasing allows players to be patient and make decisions based on the flow of the game. It helps them to cut off passing lanes and force turnovers by waiting for the right moment. In a professional context, the key phrases here are “be patient” and “waiting for the right moment.” When the world feels like it is moving too fast, hit the pause button and zoom out for a wider perspective. The thing that is moving too fast is likely you. 


The “don’t chase” concept encourages players to stay composed, trust in the team strategy, and let the game come to them instead of trying to control everything through sheer effort. Again, this presumes you already have an AI strategy in place. If this is not the case, here are a couple of thoughts to get you rolling.  
 

  1. Don’t over think it. Start with your organization’s business goals as a foundation. Your AI plans should align with the company’s priorities and strategic objectives, rather than being created in a vacuum as a stand-alone document. Also crucial here is involving your legal, risk, and executive leadership teams at the earliest stages of this process. 
  2. Incorporate AI Principles: Establishing a set of key AI principles along with the strategy will ensure there are rules of the road and guardrails to help you meet your ethical, legal, societal, and regulatory goals and requirements. While it’s useful to view other’s principles (normally listed on their web sites), it’s important to make these unique to your organization. Don’t underestimate the time required here. At Nuix we spent several weeks on this, including a detailed crosswalk of the AI regulations that are most relevant to our geographic footprint, industry, and customer base. This could work for you too. 
  3. Think Big, Start Small: While you should embrace a big and exciting long-term vision, including identifying the domains where AI makes sense for your business, start with tangible but low risk use cases—low hanging fruit—that can make an impact, generate some organizational mindshare, achieve some experiential learning, and build some internal confidence. 
     

Like the weather, information overload is unavoidable. And despite what you might imagine, no one is keeping up with all of this. I have heard PhD’s and AI experts admit that they cannot remain current because things are simply changing too quickly. Knowing this should bring some peace of mind. Don’t chase: Maintain positioning (follow your plan); conserve your energy (pay attention to what’s relevant and let the rest sail by); and maintain a healthy pace (Quality is never an accident; it is always the result of intelligent effort). 
 

Knowledge is power.  Information overload makes you powerless. 

Source: https://www.nuix.com/resources/xploreai-want-avoid-ai-whiplash-dont-chase

Xplore_AI: What’s in a name? Be specific!

by | Apr 30, 2025 | Nuix, RAI, Uncategorized, Xplore AI

XPLORE_AI: What’s in a name? Be specific! 

“What’s in a name??That which we call a rose, by any other name would smell as sweet.”?In this one famous line from Romeo and Juliet, Juliet’s heart-felt reasoning seems as poignant as it is poetic. However, naming things is a fundamental aspect of human communication, helping us make sense of a complex world. And at Nuix, data labeling plays a critical role in our AI-driven solutions, exemplifying one of our Responsible AI (RAI) principles, Specificity.  
 

Can you be more Specific? 

It may not make great literature, but adherence to the Specificity principle puts our customers at center stage, empowering them with the ability to ensure their AI models are well understood, operating properly, and in alignment with expected outcomes. In Nuix parlance, Specificity simply means that the AI tools are—or can easily be specialized to be—fit-for-purpose, which is critical to operating ethical, trustworthy, and defensible AI. One of the foundational capabilities that makes this possible is a proprietary, no-code approach that is unique to Nuix. 
 

What’s in a label? A lot more than you might think! 

In machine learning (ML), data labeling involves tagging digital assets (like images, text, or videos) with descriptive labels (like “gun,” “child,” or “patent”). This helps the computer learn to recognize and classify new, similar data correctly in the future. Essentially, labeled data acts as a guide, teaching the machine to make accurate predictions or decisions when new data is analyzed. This is an important part of building specific, purposeful machine learning models. 

Simple, right? Well, not so fast. Data labeling is just one part of a broader data preparation process (including data collection, cleaning, normalizing, etc.). And of course, all of this is a precursor to building and optimizing performant ML models. Suffice to say that these highly manual processes typically chew up at least 80% of the time spent on most ML projects using traditional methods. And this is for experienced data scientists and engineers! 

Fortunately, for our customers, the above steps can be achieved in a few clicks or eliminated altogether, because Nuix software comes standard with hundreds of pre-built models. And if we don’t have what you need out of the box, existing models can be adjusted—or new specialized models can be built—in minutes using a no-code model editor. This means subject matter experts can skip the data prep and model building phases and jump right to the ‘get actionable insights’ phase. The proper label for that phase is Inference, which is another core strength of our differentiated AI-powered solutions and will be covered in more depth in next month’s post. 


AI Your Way 

As one of our core RAI principles, Specificity is much more than an empty platitude listed on our web site, but demonstrable within the solutions we build. It ensures that our customers have the flexibility and control they need to optimize both the performance of their data analysis workflows and the defensibility of the outputs. And our customers are experiencing this firsthand, particularly those with a range of use cases or rapidly changing requirements such as: 

  • A global advisory focused on data breach response 
  • A governmental agency focused on redundant, obsolete, trivial (ROT) data remediation 
  • A top 10 pharmaceutical company focused on both investigations of IP leakage and analysis of research data to drive new drug discover and decisioning.  
  • A tax authority combating fraud. 
     
Benefits of Specificity 

Some of the key benefits of embracing Specificity include: 

  • Improved Accuracy: Using specific features relevant to the task enhances accuracy and reduces errors. By selecting or building models that are particularly well-suited to the problem can lead to better performance. 
  • Relevance: Specific models can focus on relevant data, reducing the risk of learning noise and overfitting. Additionally, models tailored to the specific task are likely to generalize better to new, unseen data within the same context. 
  • Efficiency: Specific models can be optimized to run efficiently, focusing on relevant data and features, which uses less computational power and memory and speeds up the training process and analysis times. 
  • Interpretability: Specific models produce more interpretable results, as they are built with a clear understanding of the task and data. They also tend to generate insights that are directly applicable and actionable for the problem. 
  • Regulatory Compliance: Specific models can be designed to comply with industry regulations and standards, ensuring legal and ethical use of AI. This includes enhanced traceability and auditability of the model’s decisions, which is crucial for compliance. 
     
Summary 

Specificity is a critical aspect of our RAI strategy, enabling organizations to develop and leverage more effective, efficient, and reliable models that deliver high value and meet the precise needs of their applications and evolving use cases. In a world where it can often feel like we’re ceding too much control to AI, Specificity gives our customers clarity (via interpretable insights), confidence (via improved accuracy and defensibility), and a competitive edge (via automation and time-to-value). 

Source: https://www.nuix.com/resources/xploreai-whats-name-be-specific