Guessing what's going to be new in AI for 2025

 AI has been the buzz technology for the last two or three years in the main stream with LLM's like chatgpt and a host of variants that flooded our market but as I like to guess/foresee where this buzz and real progress is going to take its kinda of fun based on reading to collate some thoughts here.  I will categorize this into three broad strokes. Large language models that have some kind of reasoning capability, ability to create virtual playgrounds with context and AI applications in scientific research. 

Large Language models Becoming more intelligent (able to reason ...kind of) 

Here’s where things get even more interesting. When OpenAI dropped o1 in September, it didn’t just introduce a new language model; it sparked a paradigm shift. Two months later, o3 took things further, pushing boundaries we didn’t even know existed. Unlike traditional models like GPT-4, which spit out answers as they come to mind—sometimes right, sometimes wrong—these new models are designed to think through their responses. They break down complex problems into manageable steps, trying one approach after another until they get it right. This so-called “reasoning” capability (we know, the term is loaded) is game-changing for accuracy, especially in math, physics, and logic. And let’s be honest, it’s a crucial leap for AI agents. What I like about this in practical applications is when chatbots to whatever agentic AI models we come up with are able to build on context and add that context to some sort of learning model and then keep building that knowledge-base in the background. 

Take for example  Sal Khan in his TED talk demonstrated this with the next evolution of Khan academy

Scenario: A student struggling with calculus uses an AI tutor for help. Older models provide step-by-step solutions to specific problems but don't adapt well to different learning styles or gaps in foundational knowledge.

With Reasoning AI: The AI identifies the student’s weak points, breaking complex problems into smaller, manageable steps while adjusting explanations to match their understanding. If one explanation doesn’t resonate, the AI tries another approach, like visualizing the problem with graphs or interactive models. Sal suggests that Khan Academy will have the ability like a personal tutor to carry a conversation with a student and make suggestions that help in thinking about problems and reason through it with AI chat augumenting it. 

Outcome: More effective learning tailored to individual needs.

AI Is Booming or a Boon to Boost Science

 AI is accelerating scientific discovery. Last October Nobel Prize winners in Chemistry to Demis Hassabis and John M. Jumper of Google DeepMind for their work on AlphaFold,  cracked the protein-folding problem, and to David Baker for tools that design entirely new proteins. This wasn’t just a win for AI; it was a massive step forward for humanity. So based on this trend in natual sciences so expect 2025 to bring a surge of data sets and models targeted at unlocking the mysteries of the natural world. Proteins were just the beginning because they had the perfect data sets for training AI. The hunt is on for the next big breakthrough—and it’s anyone’s guess what that will be.

Virtual Playgorunds 

If 2023 was all about generative images and 2024 brought us generative video, what’s the next frontier? You guessed it—generative virtual worlds, aka video games. And this isn’t some far-off dream; the groundwork is already being laid. Back in February, Google DeepMind gave us a sneak peek of what’s possible with their generative model, Genie, which turned a single still image into an interactive 2D side-scrolling game. Then in December, they upped the ante with Genie 2—a model capable of transforming a starter image into an entire interactive virtual world. And it’s not just Google. Other companies are racing to develop similar technology. 2025 could be the year where the line between gaming and AI artistry blurs completely.

Last February Genie 1 showed us the ability to transform a single still image into an interactive 2D side-scrolling game, similar to classic platformers like Mario Bros. This innovation allowed users to upload any image and watch it evolve into a playable environment, complete with logic and physics rules generated by AI. In December: Genie 2 expanded this capability by creating fully immersive virtual worlds from a starter image, enabling more complex environments, characters, and storylines. Imagine sketching a tree, and the AI generates an entire forest biome with interactive flora, fauna, and unique weather patterns. 

Another good example will  be Sony AI and Procedural Storytelling

• The Vision: Imagine a PlayStation game where players make decisions, and AI generates the story dynamically. Instead of pre-written narratives, every player experiences a unique storyline, shaped by their choices and the AI’s ability to adapt in real time.
• Practical Example: A fantasy RPG where your interactions with NPCs generate unique quests, alliances, and outcomes that vary dramatically from other players’ experiences.

Mojang Studios brings generative AI into Minecraft, and suddenly, building your dream world is as simple as describing it. I could just say, “Create a volcanic island with a hidden temple and treasure,” and—like magic—the game would bring it to life in real-time. Picture a fully realized environment, complete with fiery lava flows, intricate challenges, and treasures waiting to be uncovered. No painstaking block-by-block construction, just pure creativity unleashed.

2025 isn’t just another year for AI; it’s shaping up to be a year of extraordinary leaps. So, buckle up—the future’s unfolding faster than we ever imagined.

Data Insights - Setting SMART Goals

As a habit for continuous improvement, I want to return to this space and keep posting. Last year was very busy, hence the massive gap in not recording and writing. 

Mastering the ability to harness and interpret data is essential for success in any organization. In this post, I want to oversimplify SMART goals for my own sake to develop clear, data-driven goals that position you as the go-to person for transforming data into actionable strategies. The famous SMART Goal framework, which stands for Specific, Measurable, Achievable, Relevant, and Time-bound, will help to achieve this. Lately, I have been thinking about how an email campaign goal would be defined for one of our product's marketing goals. Let's use it as a running example to illustrate improving email marketing performance.

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1. Be Specific

Specific goals provide a clear direction and eliminate ambiguity. Define your objectives with precision so everyone involved understands what's expected. For example, instead of a vague goal like "improve email performance," a specific goal would be:
"Increase the email open rate by 15% in the next quarter by refining subject lines and targeting segmented audiences."

This clarity ensures alignment across your team and integrates the goal into the larger business context.

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2. Make Goals Measurable

Measurable outcomes are the heart of any data-driven strategy. Without clear metrics, decision-making becomes guesswork. In email marketing, measurable metrics include open rates, click-through rates, and conversions.
"Track open rates weekly, aiming for an increase from 20% to 35%, and set a benchmark click-through rate of 5%."
This ensures you can monitor progress and refine strategies as needed.

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3. Set Achievable Goals

While ambition is valuable, goals must remain realistic. Unrealistic targets can lead to frustration and missed opportunities. For example, if your current open rate is 20%, aiming for 50% in just one month might not be feasible.
"Set an achievable target of increasing open rates to 35% over the next three months by implementing A/B testing and optimizing email timing."
This approach provides a reasonable target while encouraging steady improvement.

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4. Focus on Relevance

Your goals must align with your organization's priorities. Ask yourself, "How does this goal support the business's growth or objectives?" In our example, improved email performance is directly relevant if the organization relies on email campaigns for lead generation or customer engagement.
"Focus on improving open rates because higher engagement will drive more traffic to our website, supporting our goal of increasing sales conversions by 10% this quarter."
This ensures the goal contributes meaningfully to broader business objectives.

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5. Make Goals Time-Bound

Practical goals require deadlines. A time-bound goal ensures accountability and keeps efforts aligned with organizational priorities. For the email marketing example, set a specific timeline:
"Achieve a 15% increase in open rates and a 5% click-through rate by the end of the next quarter (90 days)."
This creates a clear deadline to measure progress and maintain momentum.

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By following the SMART framework and applying it to actionable examples like email marketing, you can create measurable, realistic, and impactful strategies that align with your organization's goals and drive success.

CIO struggles: Communicating effectively to your CFO or C-Suite

During my tenure as a CIO, the yearly budgeting season was always a source of apprehension, primarily due to the inevitable query from the CFO or the C-Suite Team about the tangible benefits of our ever-expanding IT department or justifying new project initiatives/investments. This is a common concern for CIOs, who often grapple with justifying IT investments, especially when these expenditures constitute a significant portion of a company's total revenue, ranging from 1% to over 50%.

 Answering the value of IT investments is particularly challenging for many IT departments. The crux of the matter is that IT develops systems primarily utilized by other departments to boost sales, cut costs, or gain a competitive edge in the market. Typically, an IT leader might respond with a broad statement about how the IT department has supported corporate strategic objectives through various projects. Unfortunately, this claim is often unsubstantiated by complex data. So, the question remains: how should a CIO navigate this situation? 

 IT as a Strategic Business Component 

 To address this, there are two primary strategies. The first involves transitioning from a model where IT absorbs all development costs to one where these costs are allocated to user departments based on resource usage. In this scenario, IT functions as a zero-cost department, sidestepping annual budget complications. The drawback, however, is significant. This method can fragment the automation agenda, making it department-centric rather than a cohesive company strategy. This is particularly problematic with company-wide systems like AI, where the impact spans all departments. Moreover, in a charge-out system,   

IT must issue bills to each department covering development, infrastructure usage, and overhead costs. This billing process can strain inter-departmental relationships significantly if the expenses exceed budget projections. Furthermore, this approach risks incentivizing departments to seek external IT solutions, potentially leading to disjointed internal systems and undermining the company's unified automation strategy. A More Effective Approach A more effective method is to assess IT's efficacy, holding it to the same standards of corporate oversight as other departments. Like how the advertising department's impact on sales is evaluated or HR's salary system is compared with industry standards, IT should be scrutinized similarly. 

 The effectiveness of IT can be gauged through post-implementation audits of significant system projects. These audits, conducted a year after a system goes live, involve a thorough analysis to verify that the objectives and ROI were achieved. The audit process can be complex and time-consuming, especially if the original project team has undergone changes. For example, a project implementing a new customer relationship management system could be audited for its impact on customer retention rates and sales cycle times. Another example might be deploying a new enterprise resource planning system, where the audit could assess improvements in supply chain efficiency and reductions in operational costs.

 Involving Cross-Departmental Leadership and the Operations Director

 Crucial to this approach is the involvement of cross-departmental leadership and the Operations Director in the auditing process. This collaboration ensures a comprehensive and multi-perspective analysis of IT projects. For instance, the Operations Director can provide insights into how IT initiatives have optimized operational processes, enhanced efficiency, or reduced bottlenecks. Consider a scenario where IT deploys a new inventory management system.

 The Operations Director and leaders from the logistics and procurement departments could collaborate in the post-implementation audit. Their collective insights would evaluate the system's direct impact on inventory management and its broader implications for supply chain efficiency and procurement processes. 

 Involving the Risk Committee 

 An integral part of this approach is the involvement of the organization's risk committee, typically a part of the board. This committee is crucial in supporting IT investments and recognizing and mitigating risks associated with these initiatives. Their participation ensures that IT projects align with the organization's broader risk management framework and contribute to its

Security and resilience. The user department and IT might be hesitant to conduct these audits for various reasons, including potential discrepancies in the initial ROI projections or reluctance to revisit past decisions regarding headcount reductions.

 The ideal approach for conducting these audits is through an independent body, ideally part of the company's financial division. Having been involved in the initial ROI calculations, this group can ensure a neutral and accurate assessment. By adopting this method, the user department and IT are held accountable for their commitments, and the CIO can confidently respond to queries about IT investments. For instance, the CIO could report to the CFO or the C-Suite Team: "This year, we launched 10 projects, resulting in a 25% increase in sales and a 15% reduction in expenses." Now, that is a positive impact of such a conversation, not just with the CFO or the C-Suite Team but across the entire organization.

CIO thoughts :) 

Grammatically edited with Grammarly and OpenAI. (2024). ChatGPT (4) [Large language model]. https://chat.openai.com

Graphic created by DALLE

More AI Thoughts and Learning

AI encompasses many aspects. Generative artificial intelligence (AI) and extensive language models (ELMs) like ChatGPT represent just one facet of AI, but they are the well-known segment of artificial intelligence. In numerous ways, ChatGPT brought AI to the forefront, generating widespread awareness of artificial intelligence as a whole and accelerating its adoption.

You're probably aware that ChatGPT wasn't constructed overnight. It's the result of a decade of effort in deep learning AI. That ten-year period has provided us with novel ways to utilize AI, ranging from applications that predict your typing to self-driving cars and algorithms for groundbreaking scientific discoveries.
AI’s extensive applicability and the popularity of ELMs like ChatGPT have information technology (IT) leaders inquiring: Which AI innovations can provide business value to our organization without depleting my entire technology budget? Here is some guidance.

AI Options
From a high-level perspective, here are the AI alternatives: 1. Generative AI: The cutting-edge

Prominent generative AI leaders, such as OpenAI ChatGPT, Meta Llama2, and Adobe Firefly, employ ELMs to generate immediate value for knowledge workers, creatives, and business operations. Model sizes: Ranging from approximately 5 billion to over 1 trillion parameters. Ideal for: Transforming prompts into fresh content. Drawbacks: Can sometimes produce hallucinations, fabrications, and unpredictable outcomes.

2. Deep learning AI: An emerging workhorse

Deep learning AI employs the same neural network structure as generative AI but lacks the ability to comprehend context, compose poems, or create illustrations. It offers intelligent applications for translation, speech-to-text conversion, cybersecurity monitoring, and automation. Model sizes: Varying from millions to billions of parameters. Ideal for: Extracting meaning from unstructured data like network traffic, video, and spoken language. Drawbacks: Not generative; model behavior can be opaque; results can be challenging to elucidate.

3. Classical machine learning: Patterns, forecasts, and decisions

Classical machine learning serves as the proven foundation for pattern recognition, business intelligence, and rule-based decision-making, yielding explicable outcomes. Model sizes: Utilizes algorithmic and statistical approaches instead of neural network models. Ideal for: Classification, pattern identification, and forecasting results from smaller datasets. Drawbacks: Lower accuracy; the source of basic chatbots; unsuitable for unstructured data.

5 Strategies to Harness ELMs and Deep Learning AI

While extensive language models (ELMs) are making headlines, every type of AI—generative AI, traditional deep learning, and classical machine learning—holds value. How you leverage AI will fluctuate based on the nature of your business, your production, and the value you can generate with AI technologies.

Here are five strategies to employ AI, ranked from the simplest to the most challenging.

1. Utilize the AI integrated into your existing applications.
Business and enterprise software providers like Adobe, Salesforce, Microsoft, Autodesk, and SAP are embedding multiple AI types into their applications. The cost-effectiveness and performance of utilizing AI within your existing tools are challenging to surpass. Example: Imagine you run an e-commerce website that wants to offer chatbot-based customer support. Instead of building a chatbot from scratch, you can use an AI-as-a-service platform like Dialogflow by Google. Dialogflow provides a natural language understanding system that allows your chatbot to understand and respond to customer queries. You simply integrate Dialogflow's API into your website, and you have a functional chatbot without the need to develop complex AI algorithms in-house. This approach saves development time and resources while still providing a valuable AI-driven customer support solution.

2. Embrace AI as a service.
Embracing AI as a service refers to leveraging external AI platforms and solutions that are accessible through APIs or cloud-based services. These services provide pre-built AI capabilities that can be easily integrated into your applications or workflows. Example: Consider a marketing analytics company that needs to analyze customer sentiment from social media data. Instead of building a sentiment analysis model from scratch, they subscribe to an AI-as-a-service platform that offers sentiment analysis APIs. They integrate this service into their analytics platform, allowing them to quickly and accurately gauge customer sentiment without investing in extensive development.

3. Develop a customized workflow with an API.
With an application programming interface (API), applications and workflows can tap into top-tier generative AI. APIs simplify the extension of AI services internally or to your customers through your products and services. Example: A content creation company wants to automate the generation of product descriptions. They use a language generation API to create a custom content generation workflow. This API enables their writers to provide a brief description, and the AI generates detailed product descriptions, saving time and enhancing content quality.

4. Retrain and fine-tune an existing model.
Retraining proprietary or open-source models on specific datasets generates more concise, refined models that can produce precise results using cost-effective cloud instances or local hardware. Example: A retail company wants to improve its demand forecasting. Instead of building a new model, they take a pre-trained demand forecasting model and fine-tune it using their historical sales data. This approach allows them to tailor the model to their specific business needs, resulting in more accurate forecasts.

5. Train a model from scratch.
Training a model from scratch involves developing a custom machine learning or deep learning model tailored to your specific needs. While this can be resource-intensive, it offers complete control over the model's behavior and can lead to highly specialized solutions. Example: In the healthcare industry, a research organization needs an AI model to diagnose rare genetic disorders from genomic data. Since existing models lack the necessary specificity, they embark on training a custom deep learning model using their extensive dataset. This customized model becomes highly proficient in identifying rare genetic mutations, aiding in early diagnosis and treatment.

Choosing the Optimal Infrastructure for AI
The appropriate infrastructure for AI hinges on numerous factors, including the type of AI, the application, and its consumption. Aligning AI workloads with hardware and employing purpose-specific models enhances efficiency, boosts cost-effectiveness, and diminishes computing requirements.

From a processor performance perspective, the goal is to deliver seamless user experiences. This entails producing tokens within 100 milliseconds or less, equivalent to around 450 words per minute. If results take longer than 100 milliseconds to materialize, users detect delays. By using this metric as a standard, many almost real-time scenarios may not necessitate specialized hardware. For example, a prominent cybersecurity provider developed a deep learning model to identify computer viruses. Financially, deploying the model on GPU-based cloud infrastructure proved impractical. After engineers optimized the model for the built-in AI accelerators on Intel® Xeon® processors, they managed to scale the service to secure every firewall using more affordable cloud instances.

Recommendations for Implementing AI

Generative AI represents a once-in-a-generation upheaval akin to the advent of the internet, the telephone, and electricity, although it is advancing at a considerably faster pace. Organizations of all sizes must harness AI as efficiently and effectively as possible, but this doesn't always necessitate significant capital investments in AI supercomputing hardware.
1. Select the appropriate AI for your requirements. Avoid using generative AI to address a problem that classical machine learning has already solved. Example: A logistics company needs to optimize its delivery routes. While generative AI can generate creative solutions, this problem can be efficiently solved using classical machine learning algorithms designed for route optimization. It's essential to choose the right tool for the specific task at hand.

2. Match models with specific applications. Retraining, enhancing, and optimizing models improve efficiency, enabling cost-effective operation on less expensive hardware. Example: A manufacturing company wants to predict equipment failures to prevent downtime. They start with a pre-trained predictive maintenance model and fine-tune it with their equipment data. This tailored model not only improves accuracy but also runs efficiently on their existing server infrastructure.

3. Utilize computational resources prudently. Whether operating in the public cloud or on-premises, prioritize efficiency. Example: A financial institution uses AI for fraud detection. By optimizing their AI algorithms and deploying them on cloud instances with the right amount of computing power, they reduce operational costs while maintaining high accuracy in detecting fraudulent transactions.

4. Commence with small-scale efforts and secure early victories. This approach allows you to acquire proficiency in using AI, initiate a cultural shift, and generate momentum. Example: A small e-commerce startup begins by implementing a basic recommendation system powered by machine learning. As they gather data and refine their AI algorithms, they gradually expand their AI initiatives, achieving incremental successes that build confidence within the organization.

These examples illustrate how organizations can apply AI strategies to address specific challenges, leveraging a range of AI approaches, from pre-built solutions to custom model development, while optimizing costs and maximizing efficiency.

AI-Generated Lifelike Mobile Landmarks and the Expanding Horizon of Augmented Reality

The integration of Artificial Intelligence (AI) with Augmented Reality (AR) is leading to exciting advancements, notably in creating Lifelike Mobile Landmarks (LMLs). These AI-developed features reshape AR experiences, making them more engaging and interactive. This blog post delves into AI's role in crafting LMLs and their impact on AR. It includes specific examples such as Google's Immersive Maps, Cadillac's AR applications in SUVs, Google's Bard for intelligent email communication, and the innovative use of drones for terrain mapping and security analysis. AI's Transformational Role in AR Understanding LMLs: LMLs in AR are virtual elements that enhance real-world interaction, ranging from animated figures to detailed architectural designs. AI in LML Creation: AI algorithms and machine learning are crucial in designing, animating, and integrating LMLs into AR settings, making them contextually engaging and relevant. Features of AI-Generated LMLs: Realism: AI generates lifelike textures and movements. Context-Awareness: Machine learning allows for intelligent interaction with environments and users. Personalization: AI adapts LMLs to individual user preferences. AI and AR Innovations: From Google's Maps to Drones in Terrain Mapping Google's Immersive Maps and Cadillac's SUVs: Google's Immersive Maps uses AI to create 3D representations of cities for immersive exploration. Cadillac's integration of this technology in its SUVs showcases AR's potential to enhance navigation and driving experiences. Google Bard and Email Intelligence: Google Bard demonstrates AI's capability to contextually understand and respond to emails, such as crafting effective refund requests for airline ticket cancellations. Drones in Terrain Mapping and Security Analysis: A newer AI application in AR uses drones for terrain mapping and security details. These drones, equipped with AI, can scan and map an area, providing detailed topographical data. They are instrumental in remote or challenging terrains where traditional survey methods could be more practical. For insurance companies, this data is invaluable. AI-powered drones can assess risk factors, inspect damage post-natural disasters, and more accurately determine insurance premiums. Similarly, AI-driven drones offer real-time surveillance, threat detection, and security assessments for drone operators and security personnel, making them essential tools in risk management and security planning. Overcoming Challenges: Technical limitations, privacy concerns, and content management remain significant challenges in adopting AI-generated LMLs widely. Future Prospects: The future of AR, intertwined with AI, promises even more sophisticated applications. From advanced navigation systems to intelligent surveillance, the potential is immense. Conclusion The realm of AI-generated Lifelike Mobile Landmarks is just one facet of the expanding universe of Augmented Reality. The integration of Google's Immersive Maps in Cadillac SUVs, Bard's email intelligence, and the use of drones for detailed terrain mapping and security analysis exemplify the depth and breadth of AI's impact on AR. These developments signify a future where the line between virtual and real experiences blurs, offering unprecedented interaction and personalization across various sectors.

Talking about Design: Some things any kind of Designer should know

Lat month in book reading routine I hit the magic number of 8 books read. Though not meticulous about note-taking I like recording or summarizing what I have learned as key takeaways. Writing them down is another way to remind me (or whoever stumbles on this page) insights from good authors and thinkers.

In the book, I read from 100 things designers should know here are some takeaways incorporated with my own examples that may be of interest.
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People use peripheral visions to understand the context, fill in gaps and form visual patterns. It is important for designers to know whether it is stocking inventory in shopping is or laying ads on a webpage, peripheral vision more than our central vision. Patterns make it easier to sort out all the new sensory information we’re constantly bombarded with. Even if there are no obvious patterns, your eyes and brain work in conjunction to create them. Basic shapes like rectangles and spheres are identified in everything you look at in order to make sense of what you’re observing. If you were to imagine say a couple of pairs of lines or dots or even reading a jumbled paragraph with typos our mind can still comprehend and attempts to make sense of this information. Research has found if you increase the number of options in a design or a layout a paralysis of choice takes place in one's mind. Designers have realized the magic number for providing options is 4. Organizing your design elements in the patterns of three and four seem to be optimal layout but I personally think this kind of simplification is often hard to do. If you or your organization can get there - you are designing something simple but operationally to make that happen the underworkings of the carriage or the engine is extremely complex. Design and information that helps users remember are important but forgetfulness is also key for the designer to know for dissemination and consumption of information or a design layout.

User stories and logical transitions that evoke emotions and longing is another trick designers should be aware of. Storytelling is an effective way can captivate audiences because the consumers of the story have pre-programmed cortexes that try to make a chronological narrative that implies causation. This can be used to your benefit. Since the human brain is constantly looking for patterns, it fills in gaps by making leaps of causation. The formula of “this caused that, then this happened, then that” – the basic pattern of any story – is easy for the mind to follow. Over 2,000 years ago, Aristotle came up with the three-act story structure. The beginning sets the scene by explaining the characters and situation; the middle provides obstacles for the characters and a means of resolution, and the end shows the climax and conclusion. Moviemakers, scriptwriters, dramas often follow this pattern.
Use stories and clear organizational systems to make ideas suitable for long-term memory.

When designing your product or interface remember people crave empathy at different levels that follow established social rules. For example when you smile at a stranger often a stranger will smile back. This is because of mirror responses where the premotor cortex activates the mirror neurons causing the person to smile. Imitation and empathy are the way people connect with others and adherence to socially established rules is often followed. Hence designing for cultural context and sensitivity becomes a huge consideration. Advertisers for international brands learn this the hard way. There is a famous case study of Toyota's launch of a car and the advertising campaign didn't work in China plummeting and a design decision that led to the killing of that product but huge losses for the company. When designing a product or a campaign, it’s essential to think about the interactions your audience will have with it. Make sure it follows the rules of social interaction!

The book also talks about the wandering state of people's minds and incorporating a flow state in your design. A study undertaken at the University of California found that people think their minds tend to wander 10 percent of the time, when, in actuality, it’s more like 30 percent of the time. It can even be as high as 70 percent – say, if you’re driving on an empty highway. So when you’re in the process of designing, it’s vital to remember that people’s minds wander and that they’ll only focus on something for a limited period of time. So if you’re designing a website, it’d make no sense for the welcoming page to be dominated by dense blocks of text. People simply won’t read it. It’s wiser to break up the information with images, play with the text format or include other media such as video. This will give your audience the illusion of wandering while staying focused on your product. I like Google's about page and a lot of websites today use the big visual image concepts and very little text to convey their information fast for various devices and platforms. However, the flow state is the polar opposite to the wandering state of the mind. Designing for flow states of the mind is understanding how to give those quick dopamine releases and feedback loops quickly. Social engines like Facebook, Twitter are examples of causing small dopamine releases in your mind when someone likes your pic or tweet. Apps that give goal orientation and feedback for showing achievement often get traction. I am always impressed by coffee shops or smoothie makers who give cards to their patrons with two stamps already marked or showing a simple tracker that gets you that free drink is novel ways of incorporating the flow states of engagement.

Recognizing people like having options and restricting those options to the magical numbers so as a designer you don't overwhelm or create the choice paralysis is a key to good desingn practices. Lastly incorporate unpredictability as that is another trick in a good designer to stimulate the production of dopamine. I have seen this in good game designers and puzzle makers who take the inherent human need for the quest from easy to higher levels of complexities. Incorporating surprising elements and cues in your user interfaces and product design keep people coming back.

Design - Abstraction

I am a big fan of UI/UX interfaces especially when it comes to software applications but I think of Design not from a web, print or even product perspective with rules that follow the principle of form follows function;  but interfaces or products that also go the other way around where function can follow form and evoke delight, emotions and holds for the user a sense of belonging, pride and deep yearning to return to use it more often.  Harry Beck was a 29-year-old engineering draftsman who had no idea about user interfaces or user experience principles yet contributed a breakthrough

invention with his design of the London Tube map which has become the defacto standard of all metro maps around the world.  The earliest designs of the London metro had landmarks of trees, museums and familiar places that people would know of. Beck's important design insight was when he realized that people do not care for these symbols when they are underground. All they want to know is where to get off and where to get on at a given station. He simplified the design in horizontal and vertical lines spaced the stations equally, color-coded the lines and routes like an elegant electrical circuit. This simplified abstraction of a user interface initially became pocket maps for early commuters and still followed by modern-day tube maps.

Programmers are very familiar with the concept of abstraction. In design principles for products or interfaces coming in the middle of abstraction is where the success of masterful design lies (at least that is how I comprehend it). European school of design be it in the crafting of writing instruments or the most beautiful car design have this inherently built into the craft of their production cycles. Make it too abstract the product or system becomes unusable, make it too realistic the design of the product or system becomes boring. Abstraction is coming in the middle where it might not be exact or perfect but user acceptance and connectivity with the design come to a place of universal embracing.