Sorry, there is no better. They are just different. Both use the same OpenAI large Language model.
The key question is: Do you prompt for yourself or do you create prompts for others in a business environment”?

ChatGPT

When you write prompts for yourself, you may prefer ChatGPT.
Why? it’s quick and you polish it until the results are good for you.

• Your prompt library is a long list accessible only to you.

GPTBlue

When you create prompts for others, you may prefer GPTBlue.
Why? It’s usually in a business environment, where you compose prompts for others.

• Productivity gain must be visible and measurable.
• Prompt Composition allows us to develop unique prompt features.
• Library networks are key in larger organizations. It’s part of GPTBlue today.
• Partner integration and productivity induction is becoming a strategic advantage.
• Analytics will help you and your users better understand the performance.
• Business Model allows prompt designers to sell the prompts with recurring revenue.
• Future Evolution looks like prompts will evolve into entire applications.

Learn more about this feature-rich solution on the GPTBlue web page.
If you develop prompts for other’s, try it out  it’s free

ChatGPT versus GPTBlue

It boils down to prompting for yourself or designing them for others as profession.

ChatGPT versus GPTBlue Architecture

GPTBlue is leveraging the Open AI Large Language Model (LLM) and its, Generative Pretrained Transformer (GPT). The value-add comes from an AI-Native application that sits on top of it. The prompt framing architecture frames the prompt functionality given by the LLM but allows additional prompt design features on top of it. The prompt composer

GPTBlue AI Network

Now, here GPTBlue goes really crazy. Based on the needs of some very large customers we developed a unique Gen AI Application Network. It started with the question how can we better deal with the rapid proliferation of prompts where nobody knows who has them, who wrote them and are they productive? A multi library network was a starting point. Rules for certain prompts to deploy to specific libraries only. The integration of business partners was next. It ended up with thousands of users, hundreds of libraries, central observation and decentral management. You will find more in our case study, available Beginning of March.

Because we are a highly productive AI company, we have far more time for our customers

#AI #GenAI, #PromptEngineer #Prompt #GPTBlue #BlueCallom #ChatGPT #PromptLibrary #PromptProductvity #PromptAnalytics

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In 2023, Generative AI marked a significant milestone in AI’s evolution, with an impressive user base of around 200 million on ChatGPT alone. This year symbolizes the inception of a new era in software development, transcending the earlier transition from on-premise software to Software as a Service (SaaS). AI is not just altering the software delivery model; it’s reshaping the very foundations of software design. What began in AI consumer software is transforming business software in 2024, signaling a new software paradigm.

The Challenge with Conventional Business Software

The complexity of business software, especially in larger organizations, has reached a point of critical reflection. Many executives and board members express concerns about its evolution over the next decade. The appetite for “monster software” investments is waning. The business world, including mid-sized and small enterprises, is in dire need of simpler, faster, and less complex tools.

The Future of Business Software – a significant deviation.

Today, AI, particularly Generative AI, appears as the logical solution to remove complexity. However, it’s not about replacing one type of software with another. It’s about understanding the unique capabilities of Generative AI. Unlike conventional software, designed in a sequential linear fashion, our brains operate differently. Our Neuro-AI-Fusion model was developed to mirror the human brain’s creativity and ingenuity. This approach has enabled teams to generate disruptive innovations in weeks instead of months, potentially revolutionizing software development itself.

On January 29/30, 2024, we’re excited to introduce Neuro-AI-Fusion to Prompt Engineers at our Prompt Engineering Boot Camp. We’re transforming PROMPTS into AI Components within BlueAICONs – easily connectable, replaceable, adjustable, and maintainable structures. These represent a radical departure from conventional software design, evolving into an AI Application Network accessible to all employees, regardless of technical expertise.

Introducing GPTBlue: The Core of AI Components

At the heart of AI Components is the PROMPT – the crucial element for AI understanding and execution. Developing, testing, and deployment cycles are key to creating effective prompts. GPTBlue, our professional Prompt Management System, caters to these evolving professional requirements. Imagine having the capability to personalize responses to social media posts rapidly or to intelligently sort through hundreds of job applications – all with a single prompt in GPTBlue.

Now – Think of Professional Social Media Support functions. We are having a way to comment on 50 posts within minutes with a post-specific and very personal touch. A post Comment Creator Prompt can do that. In our system, it is a single prompt.
Or think of your Talent Development Team, needing to review hundreds of job applications. Having a way of letting the AI do a first match and a smart ranking and rating instead of just a “go or no go” is of great help. Also, this is a single prompt created on GPTBlue.

We have developed nearly 100 powerful prompts, shared as source code for customization in GPTBlue. These prompts span various departments, offering significant efficiency and productivity boosts.

To make it easy for any starter, we crafted approximately 10 Delta Prompts for every typical department we came across. We classify prompts into different groups. For instance, A DELTA PROMPT is a powerful prompt that makes a difference and specifically increases efficiency and productivity.

Developing Your Own DELTA PROMPTS with GPTBlue

GPTBlue DTD is our development, testing, and deployment engine for crafting powerful prompts. This new approach to AI application development is suitable for enterprises and small businesses alike. BlueAICON Prompts can be shared electronically, simplifying the process for end-users who receive tailored, case-specific results.

A New Era of Professional Prompt Engineering, Testing, and Deployment

Consider a business scenario where GPTBlue PFD-Stations (Prompt Framing Designer) are used internally and by external partners. These powerful prompts can be customized for different markets or business needs. With version control, history management, and other features, GPTBlue PFD is a comprehensive tool for prompt development and management.

The GPTBlue PFD (Prompt Framing Designer) is the heart of prompt development. However, larger organizations with country offices, departments, business units, and possibly external business partners need a far more organized way of generating, releasing, and deploying Prompt or AI applications.

BlueCallom’s GPTBlue Network Architecture was developed to serve large enterprises and networked sales channels. The end users in departments or partners around the world don’t need any training, A BlueAICON will deliver its own help and support info as part of the prompt to the user. A sophisticated billing system is integrated into the corporate deployment network so departments can be charged individually based on the usage of the AI system which is part of the overall cost.

The Future of BlueAICON

BlueAICONs represent a sophisticated level of prompts, functioning as AI Components Over Networks. These include supportive information, visual aids, and user rights, and can act as Edge Computing Clients. Our focus on leveraging edge computing will be a significant aspect of our future developments.

Partnering with Professional Prompt Engineers and Consultants

Through BlueCallom’s Business Partner Program, businesses of all sizes can harness the power of the GPTBlue series. This includes developing professional prompts, deploying them to customers, and offering support and maintenance services.

[su_button url=”/bluecallom-business-partner/” target=”blank” background=”#ffffff” color=”#007abb” size=”8″ wide=”yes” center=”yes” radius=”5″ icon_color=”#f8f8f8″ text_shadow=”0px 0px 0px #000000″ class=”font-weight: 900″]Read more about partnerships[/su_button]

Join the Future of Business Software

This is an exhilarating time at BlueCallom. With the launch of GPTBlue, we invite you to join our journey, share your ideas, and influence the future of BlueCallom. Be part of our community in the World Innovations Form and engage with the BlueCallom “Circle.”

We welcome your thoughts, preferences, and suggestions for improvement.

Thank you,

@AxelS

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Human Intelligence Augmentation (HIA): The Future of Human-Machine Collaboration

The Dawn of HIA In 1961, Douglas Engelbart introduced the world to Human Intelligence Augmentation (HIA). Instead of technology replacing humans, Engelbart envisioned a symbiotic relationship, where machines would amplify our capabilities. While the technology of the time couldn’t fully realize this vision, today, we stand on the verge of Engelbart’s dream.

The Essence of Intelligence Historically, intelligence was viewed as our ability to acquire and apply knowledge. Today, our understanding is more nuanced; intelligence is multi-faceted, encompassing logical reasoning, creativity, emotional intuition, and more. While humans possess unparalleled creative problem-solving abilities, our capacity to handle hyper-complex problems is limited. On the other hand, AI excels at processing vast amounts of data efficiently, free from human fatigue and biases. But AI lacks understanding, empathy, and purpose-driven context. The solution? A collaborative approach, maximizing the strengths of both humans and AI, holds the key to untapped potential. In all of human history, homo sapiens turned out to be a master in building tools to overcome virtually any physical deficiency. Humans can’t lift tons of weight, so we created a crane. Humans can’t run at 100 km/h to get fast from A to B. So, we built cars after we created wagons pulled by horses. We cannot easily comprehend what is involved in creating breakthrough innovations, so we built computer software that can help us think through any aspect of the process. Today, we are at a point where, with the help of AI, not only can almost everybody do that task, but we can do it in a few weeks instead of needing a year or two. The Future of Mind-Machine Collaboration starts today.

Unleashing HIA’s Potential Several technologies are pushing the boundaries of HIA. Among these is Neuro-AI-Fusion, which leverages insights from neuroscience to activate brain triggers, enhancing human thinking. We’re also seeing advancements in Generative AI and Large Language Models, enabling a higher level of creativity and processing capabilities. Remember, the AI tools that are transforming industries today are augmentations of our abilities, just like vehicles or cranes have been in the past. As we gaze into the future, HIA promises innovations in record times, individualized learning experiences, and solutions to massive challenges like space colonization or environmental crises.

HIA in the Business Sphere The business landscape is changing, with a demand for rapid innovation and a growing skills gap. HIA has the potential to revolutionize this sector. For instance, managers can simulate decision outcomes akin to crash testing in vehicles. Moreover, with HIA, businesses can offer personalized customer experiences, optimize human resources, and foresee supply chain disruptions, giving them an unparalleled competitive edge. The era of raw data reliance is fading; businesses will soon harness the blend of human insight and AI precision. This blend is already realized today when predicting innovation success probability.

World’s first HIA application  A very timely example is the challenge of getting AI into all areas of a business to reduce the workload where employees are not best qualified and delegate those tasks to machines. The time gain allows everybody to do more creative work and increase productivity by an order of magnitude. In a way, everybody will become a manager, managing machines by delegating all kinds of work like research, report writing, documentation, review processes, validations, and much more to AI-driven systems. Such an AI Adoption Project may have taken one to three years in large global enterprises. Today, BlueCallom ADOPT helps teams manage the execution of such a project in a few weeks and delivers a magnificent outcome that is almost guaranteed. It is the first genuine Human Intelligence Augmentation solution leveraging Neuro-AI-fusion for direct brain access without touching the person. The Future of Mind-Machine Collaboration is now a reality.

HIA: Transforming Education Education disparities exist globally, with many facing unequal access to quality learning. HIA offers solutions: adaptive learning platforms, augmented reality tools for remote education, and AI-driven materials tailored to various cultural and linguistic backgrounds. HIA tools can bridge expertise gaps in specialized fields through simulated learning environments, AI-driven expert systems, and collective problem-solving platforms.

HIA: Rise of Super Humans  Even though HIA does not touch the brain or involve any medical or other modulations, it gives humans superpowers relative to anybody else. It allows individuals and teams to do things in a fraction of the time or reach ideas that are just not possible with brainstorming or other methods. Again, the first people who drove a car could span distances nobody else could before. Or people could fly what was impossible even to imagine before the inception of the first planes.  Mind-machine collaboration is now on its long journey to a world unimaginable for most of us.

In Conclusion  HIA isn’t about machines replacing humans or keeping us occupied in a technology-dominated age. It’s about embracing technology to enhance human abilities and forging a collaborative future. Through HIA, we’re charting a path to a future where humans are more insightful, connected, and capable. Embracing this collaboration doesn’t just redefine our work or learning; it reshapes our very essence in a digital age. As we venture into this intertwined future, we have to realize that HIA  is not a way to protect humans from machines; it is all about giving humans yet another power tool to do things that we cannot do with our abilities. But this time, it isn’t augmenting our muscle power but our brain power.  Hence our saying: AI is not replacing people, but people using AI will displace people who don’t. You have very, very limited chances to become a manager if you can’t read or write. The next generation, leveraging Human Intelligence Augmentation, will displace anybody without exception.

Meet the Mind-Machine Collaboration Human Intelligence Augmentation is here today and is demonstrated on Nov 30th at the Swiss Innovation Forum in Basel. You will be able to have a first live HIA experience at our booth by solving a complex problem within 180 seconds. You will become a Super Human for a few minutes without anybody touching you – it will blow your mind. Make sure you register for a time slot. The event is only one day !!!

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AI-driven Innovation Paradigm Shift II

As the era of AI unfolds, there’s an increasing discourse around job losses, with many fearing machines might render humans obsolete. However, a nuanced perspective indicates that AI is not directly replacing jobs but’s revolutionizing them. Professionals armed with AI tools are set to outpace their peers who rely on traditional methodologies. So it’s not AI that replaces jobs but people who use AI. Here’s how:

1) Marketing Plans
Imagine a world where creating a comprehensive marketing plan is as quick as making your morning coffee. AI can be fed parameters such as target demographics, budget constraints, and company goals. Within the hour, one might receive a detailed marketing strategy. This includes research-backed insights, potential outreach channels, predicted outcomes, and even content suggestions. While humans are pivotal in feeding contextual details and verifying the plan’s alignment, AI dramatically shortens the time and effort involved, turning a week’s task into merely an hour’s.
The human role: Let the AI know what YOU want in that marketing plan and what constraints YOU want to give the AI. Without intelligent input, neither a machine nor another human can perform that task.

2) White Papers
Once considered a tedious, time-consuming task, white papers can be made efficient with AI. Researchers or marketing professionals provide AI with a theme, objectives, and a few essential details. The system, equipped with access to countless databases and research journals, drafts a white paper complete with research references, infographics, and a well-structured narrative. The human role evolves from tedious drafting to primarily conceptualizing and verifying, cutting the production time from days to hours.
The human role: Let the AI know what YOU want to communicate for an audience that YOU specify. Also here: without meaningful inputs, neither a machine nor another human can perform that task.

3) Product Description
For companies with a vast array of products, writing and updating product descriptions can be daunting. With AI, a product’s raw data – size, features, benefits, price, and images – can be input. The AI then crafts compelling, SEO-optimized product descriptions in minutes. This not only standardizes the quality but exponentially speeds up the process, allowing businesses to launch products faster and adapt to market changes swiftly.
The human role: YOU give the AI the PURPOSE and it will do the work accordingly.

4) Event Landing Page
Event managers and organizers can breathe easy. When tasked with creating a landing page for an event, AI can assist in designing, scripting, and even optimizing for conversions. By simply feeding the system event details, target audience specifics, and desired outcomes, AI can craft a visually appealing, content-rich, and user-friendly landing page. Instead of laboring over design and content iterations, humans can now focus on strategy, outreach, and making the event a success.
The human role: Have the idea you want a landing page.

5) Training Class Presentation
Training modules can be made more engaging with AI. Trainers provide the core content and desired outcomes. AI can create a dynamic presentation complete with visuals, case studies, interactive elements, and even quiz sections. This ensures consistent quality, aids in better knowledge retention, and most importantly, lets trainers focus on delivering the content rather than the intricacies of presentation design.
The human role: Understand that a training class presentation is needed and YOU tell the AI how it should be done.

6) Legal Document Review
Legal professionals often spend hours reviewing contracts and legal documents. AI can analyze these in minutes, highlighting discrepancies, potential risks, and areas of concern. Lawyers can then concentrate on strategy and counsel rather than the tedious task of document analysis.
The human role: Review and validate the AI output rather than do the digging and searching yourself.

7) Personalized Learning Paths in Education
Educators can leverage AI to craft individualized learning plans for students. Based on a student’s performance, strengths, and weaknesses, AI suggests a tailored curriculum, ensuring more effective and adaptive learning experiences.
The human role: You get the idea: no matter what – Somebody needs to set it in motion

8) Real Estate Analysis
Real estate agents, with the aid of AI, can provide clients with comprehensive property analyses in record time. By entering property details, AI can predict market value, estimate renovation costs, and even suggest optimal selling periods.
The human role: Same here  – set it in motion

9) Fashion Design
Fashion designers can introduce AI into their creative process. By feeding current trends, preferred fabrics, and desired outcomes, AI can generate design sketches, patterns, and even predict market reception, allowing designers to be trendsetters at an unprecedented pace.
The human role: And here  – set it in motion

10) Film and Video Editing
Film editors can provide AI with raw footage, desired mood, and thematic elements. The system can then generate a rough edit, dramatically speeding up post-production timelines. This ensures quicker releases and lets filmmakers focus on storytelling nuances.

The consistent thread across these transformations is the paradigm shift from manual to collaborative intelligence. As AI and humans collaborate, the boundaries of what’s possible expand, ushering in a future defined by efficiency, innovation, and unprecedented achievements.
The human role: And also here  – set it in motion

I guess you can now imagine that those 10 examples can be applied to thousands of jobs. If you can’t see it give it a try or ask Chat GPT or ask me.

What does it mean for you?

If you are in any office job, you will learn to become a manager, no matter what you do today. You will become the manager of your AI system and tells it what it should do for you. This may be writing a report, summarizing today’s calls, creating a document, making a plan, telling it to process information, asking it how you can do what you are supposed to do, ask it to teach you any task, find out how things work, and draw an image even if you can’t even draw a tree. As you advance, you ask the AI to create a presentation for you or your boss, or to create a video about any topic, or analyze a project, analyze a process, suggest a process and prepare every item that needs to be done to complete it. Probably half of your daily work will be done in 10 seconds for each piece plus the 5 minutes to explain it what you want. All you need to learn is to `tell it what you want and most importantly learn to know and say what you want. This new ability will pen op new job opportunities and remove barriers like “I’m not good at writing letters” or “I’m not good at drawing images” or “I’m not good at analyzing data”…

Addressing the Global Skills Shortage through AI

Use the team you have – but much smarter than ever before !!!

AI-driven Innovation Paradigm Shift II  delivers also a highly crucial aspect on employee quality. The global skills shortage, a pressing issue in today’s rapidly evolving job market, threatens economic growth, innovation, and competitiveness across nations. As industries advance and new specialties emerge, there’s a widening chasm between the skills available in the workforce and those in demand. While retraining and upskilling initiatives are paramount, they are not the sole solution, especially considering the urgency and scale of the issue. Enter Artificial Intelligence — not as a replacement for human talent but as an amplifier and leverage for less skilled people. Somebody who never created a blog post can still ask the AI how a blogpost is written and than feed some bullets to the AI. The AI can be used as force multiplier. AI can tailor educational content for faster, more effective learning, predict which skills will be in demand in the coming years, and even assist on the job, guiding workers through complex tasks until they’re proficient. Furthermore, AI-driven platforms can match individuals’ potential and transferable skills with emerging job roles, bypassing traditional credential requirements and emphasizing capability instead. By harnessing AI, we’re not just bridging the skills gap; we’re reimagining a future where talent and technology converge, ensuring a resilient, adaptive, and skilled global workforce.

_____________________________

An important note at this point

You may have guessed it already, This post was written by AI, we use, GPT-4, and leveraging our Prompt Framing technology. All I had to do is compile a list of things I wanted to talk about and a minute later I had the results. To do the post it required still me (a human) to say I want a post. It required me to give the AI the structure I wanted and the bullet points in order to get this output. And finally the review and some personal touch like this paragraph. All done in about 20 minutes.

The learning for Business Leaders

AI is not just another piece of Software the IT team should get familiar with, but also the most revolutionary organizational innovation instrument since the industrial revolution with its production automation. I use the term instrument very purposefully. Like the conveyor belt changed mass production from the ground up, it needed to be implemented. it needed to be part of the organization and it needed to be part of he company culture. All this was possible with a few drawings, lists, new rules and organization descriptions. Today’s enterprises are so complex and optimized that it needs tools to make it work.

Leading by innovation

It takes leadership to transform a company through a paradigm shift. And this one is far bigger than most expect it to be. Leading by innovation requires the understanding about how AI really works. Neither the “Dooms Day Prophecy” nor the “Click and be done” mentality is helpful in any way.  The future of work lays not in the hands of politicians, scientist or educators. It is in the hands of CEOs. They are the ones that let a company crash or flourish. They need to look into what AI is doing like the farm boys looked at industrialization 200 years ago.

Like the CEO of a car manufacturer knows what cars are, he or she also know what SALES means and how it works, or how FINANCE  ticks and what it means, or how MARKETING does the job. Now there is a quantum leap in organizational management, time management, and results amplification. That requires skilled people we just don’t have – and we don’t need to.

PART III

AI-driven Innovation Paradigm Shift Part III — Organizational Innovation
I noticed that it may be helpful to describe how companies can initiate that paradigm shift, set new goals and empower people to complete far more jobs in the same time by leveraging AI. Also what task can be performed by less educated people and how all that will drive down cost and increase profitability.

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It was about the year 1435. countless monks were copying books. They did so by writing a new book by hand from scratch. Hundreds of pages. Monitored by supervisors, ensuring there is no single mistake on page 563, for instance. Otherwise, the monk had to rewrite it. This entire business of educating the world has suddenly changed. Around 1440 Johannes Gutenberg introduced the printing press. It was the first major shockwave that hit monks like a stroke.

One of the first modern innovations

Many thought that this machine needed to be destroyed. But it turned out the other way around. Within a few years, what was considered a curse became a blessing. All of a sudden, not only a few monasteries with a rare number of educated writers could distribute knowledge. Monasteries from all over Europe could quickly replicate knowledge and distribute it in their community. This was the very early start of the knowledge explosion.

More and more monks were needed to create new and unique content; others were busy with printing, yet others with producing more paper, packaging, packing, and shipping. It was no longer a business for just a few educated writers but for monks and, later on, normal people in the villages participating in that business.

The printing press led to the need for more people – not less. It led to more business opportunities, sales channels, and better education, which allowed more businesses to flourish. It is one of the best descriptions of disruptive innovation. AI is precisely of the same fabric. It will help us do things in any industry and any aspect of our lives that were impossible before. I don’t want to overshadow the risks. Analyzing those risks and, for the first time in history, preventing those risks while innovating is part of the paradigm shift. Innovating and automatically conducting various risk analyses at every major step in the process creates sustainable innovations with transparent risk profiles.

Why is the above analogy so relevant in today’s innovation and artificial intelligence world?

The old innovation paradigm versus the new

1. Try and error vs. strategic and tactical innovation.
2. Innovation is done by the same “experts” who would need to question their own expertise vs. by a diverse team of differently wired backgrounds and specific cognitive abilities.
3. Today, all repeat jobs like administration, KPI monitoring, Reporting, and Guidance are done by humans, versus an AI system that can augment human capability.
4. Ideation is reduced to a person’s experience (see neuroscience) instead of augmented by an AI that cannot really generate ideas but stimulate the ideation process.
5. Research is done manually today by researching relevant content, revising and abstracting the critical pieces, and summarizing the result, which may take weeks or even months, instead of by an AI that can do all that in seconds.
6. Today, disruptive ideas are created by hoping and wondering, dreaming and thinking in random exercises and models like design thinking versus neuroscience-driven ideation that stimulates the brain’s natural process and is supported by AI for internal and external idea confluence that unfolds breakthrough innovation and in the same process disruptive business models.
7. Ensuring that the disruptive solution is a market fit, stopping market research like idea validation, and instead using neuroscience-driven methods to have an undisputable result.
8. Today, 80+% of disruptive products or services fail in the market for many reasons that may have their roots anywhere from original idea to final product or go-to-market strategy. Instead of moving it back into the existing organization, let the innovation team bring it to market, leveraging AI to its fullest extent, which would be impossible in an existing organization.
9. Instead of buying a startup with 100 people or even less and hoping they can bring innovation to 20,000+ employee enterprises, create a detailed ‘Innovation Meta Strategy’ for the corporation and then decide what you really want and how to make it happen.
10. Instead of pretending you have an internal startup, think big and build an internal Unicorn. And instead of looking for improvements that will never be mentioned in an analyst report, create a ‘Large-Scale Innovation’ that changes how people do things that were never possible before.

From the first innovation opportunity discovery to scaling in a global market – the new Innovation Paradigm dictates an end-to-end solution that is thought through in all aspects of what it takes to make an innovation successful like any of the far over 1,000 unicorns already do. NEVER try small steps or walk before you run. There is no trying with pregnancy, and you cannot try a circumnavigation with a sailboat unless you already did the first.

Leading by innovation

It takes leadership to transform a company through a paradigm shift. And this one is far bigger than most expect it to be. NVIDEA was a startup in 1993 that was shining through the innovative graphics accelerators for gaming systems it offered. Twenty years later, it was one of the top tech giants in Silicon Valley. Ten years after that, it joined the Club of Valuation Trillionaires. With one exclusive feature: Continuous innovation.

Examples of the AI-driven Innovation Paradigm Shift

All images in this post were created with an AI from #MidJourney. It was done in 20 minutes, including several tries to get the desired output. So I took away business from our ad agency or graphic design freelancers. Well, here is MY POINT: did I really?

NO – I did not take jobs away from graphic designers. I only took the job away from graphic designers stuck in the old paradigm of taking hours and hours, rounds and rounds of reviews and arguments, whether it should be this or that.

MODERN GRAPHIC DESIGNERS use tools like MidJourney, Dall-E, Jasper, Canva, and many others. They can produce an image within 10 minutes, including reading the order. If they charge $20 each, that is $120 an hour. Way more than they earn right now. The only difference: 20 times more productive.

MODERN TEXTER, leverage ChatGPT or similar Large Language Models. They let the task through GPT-4 and get an excellent text, press release, and announcement… That costs 60 seconds + 2 human reviews, and you are done in 10 minutes instead of 3 hours. You charge $25 per press release and make $150 per hour. Again more than most copywriters. Also, you do more with less and earn more than you did in the past. Leveraging modern tools are increasing your productivity 18 times.

MODERN CFOs leverage ChatGPT or their own GPT-4 models to analyze financials, predict cash flow, predict ROIs, and much more. In this case, the CFO Is not in danger but can do more things in less time. But – that leads to a situation that old-world CFOs may need help staying in business.

MODERN INNOVATION TEAMS, ok, this is my favorite. Modern innovation managers use AI and neuroscience discoveries to get to top-notch innovation opportunities in the first place within days. In a few more days, they get all the research done and then take two weeks to craft a breakthrough innovation, and develop the corresponding disruptive business model. A few days later, they get their market validation and go for an innovation financing meeting with the CFO. And again, what took easily 6 to 12 months for complicated processes, unbearable administration, KPI collections, and reporting is now done in about three months. The same goes for prototype building, go-to-market, and scaling. Most breakthrough innovations require 10+ years to see an ROI. Today it is no more than three years. The newly won productivity gives you a 10-fold increased productivity in innovation and a three-fold ROI acceleration. Adding the Innovation failure rate of about 90% into the mix, your Innovation output increases by 100x.

IS IT ALL MARKETING BS? I know – those numbers look too good, and most arguments we hear include, we are an enterprise and tick differently, legal requirements, compliance, regulations, etc. But when you look under the hood, some arguments are based on distrust that your solution may not work, that nobody knows if your idea is really disruptive, uncertainty that the market will not accept the product, that customers won’t pay the price you anticipate, and roughly 70+ more challenges. Correct, but did you ever consider changing ALL those “circumstances”? No? But you can. This is why we use the term “Innovation is a Paradigm Shift.” The CEO and the board can make a significant difference in making innovation much easier, yet you must ensure it will work.

Other arguments are process and technology related and include: “We need too much time for reporting and KPI collection,” “We spend too much time for research,” old technology, old processes, old methods, old management… And those arguments can be alleviated even faster.

Neuroscience provided a game-changing inspiration. How can you THINK if you don’t know how your brain does it? How is the brain stimulated best? Where are its limits? And Artificial Intelligence is the other big game changer. Now we are leveraging AI for a productivity level that is by an order of magnitude higher than ever imaginable. Fusing neuroscience and AI made the Innovation Paradigm Shift and empowered us and our innovation teams to perform a quantum leap in Innovation productivity. Moving from guessing and hoping to repeatability, manageability, and predictability.

This is no future thinking but a reality today. BlueCallom’s AI system help you research, create exceptional ideas, and make intelligent decisions. And yet another AI system that automatically manages and monitors KPIs and prepares reports and performance feedback. Now the innovation teams can focus on one thing only: Their nature-given ingenuity. Here and now.

The AI-DRIVEN INNOVATION PARADIGM SHIFT is like the Gutenberg printing press. Instead of manually crafting one innovation in sequential stage gate steps and doing every single step on your own, now create a Meta Strategy for the entire enterprise, and use an AI-driven innovation that guides you through that process. Instead of shrinking innovation teams, the teams can make far more innovations, and they don’t need to be all top experts but now can be a mix of diverse backgrounds. Large-Scale Innovation like Peta-Watt Power Plants, High Volume Intelligent Logistics, New Monetary Systems, Disruptive Insurance Models, Modular Mobility Technology, Collaborative Moon Base Infrastructure, Harvesting Asteroids and other Planets, and developing new and individual organization models is no longer Sci-Fi. Sustainable AI with the ability to make its own risk assessment or assess the risks of other AI Models will turn the fear into a new perspective. Businesses not only get more productive but can build and do things that have been impossible so far.

In PART 2 I will talk about how all this may shape the future of work and our education systems.

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Prompting for Newbies

If you are a savvy “prompter,” you can skip this paragraph. For those who are not that familiar with how to enter good questions into ChatGPT, this might be helpful.  ChatGPT is the most advanced way to search, and get clear answers to virtually any imaginable question that you could ask “The Internet”.  You can obviously ask a question by typing, “Who are the biggest competitors of Google? You get a decent answer. But when you are more specific, asking for competitors in the AI space, you get a different answer.  And if you go further you could ask:
“I want you to be my market research assistant.
I will tell you about my research needs and you will create a table with the following columns:
Ranking | Company name | company location | main business focus.
Sort the list by Company Name in alphabetical order
My research needs are as follows:
Find the 10 most relevant AI companies that are competing with Google.

You will get a nicely formatted list.
Now ask a very simple follow on question:

“Show me the same list for European competitors.”

You notice it kept the dialog between you and the AI and completes the task with all the previously defined features.

The ability to write those specific requests like the first one and know how to possibly follow up is called “prompt-Engineering’ The prompt is a well defined part that prompts the AI system into a specific persona, task and form for instance. And this is just a simple example. Unlike some other opinions, prompt engineering is not going away. It is the language in which we communicate with AI systems, and it will create results based on our input.  Understanding Prompting*, Prompt Engineering** and Pre-Prompts*** doesn’t require to learn a new language. It simply helps enormously to make it very clear what you want. And that is not only with Artificially Intelligent Beings but also with Naturally Intelligent Beings like you and me

Three areas of AI-aided Innovation

During an innovation process, Innovation Teams are working on thousands of different things. This makes innovation a bit difficult. It is very interesting to realize, we can group almost all those task into three innovation relevant action groups:
RESEARCH — VALIDATION — IDEATION

ChatGPT Prompting for Innovation Teams for RESEARCH

Before you start solving a problem and trying to develop a disruptive solution,  you will want to research the problem you are trying to solve and understand it from every aspect.
The old way of doing research is to go into a search engine, look for background information, scientific papers, research documents, user reviews, make some sentiment analysis in the social web, and so forth. Then after reading through all the documents you summarize key findings, prioritize certain findings, and craft your assessment. It takes at least several days if not weeks. In a typical innovation process we find at least 14 areas, where you need some research – not only in the beginning. All in all, it is approximately 2-3 months of uninterrupted work, to do all of your research with your team.
The new way of doing research is to ask questions to a Large Language Model like ChatGPT and get the answer immediately. You may want to consider some 30 minutes to finetune your question and still validate the outcome. But 14 research occurrences, times 1 hour is two days and not two months. You just collapsed your innovation research by 95% (2 days instead of 40 days). Even if you need to work on it for a few weeks to get savvy – the gain in efficiency is phenomenal. Also a great example for how AI is not replacing people – but hyper efficient people using AI will replace people who don’t.

To improve it further, you can optimize not only the research itself but the preparation from research reports. Tell the AI to serve the findings in nice tables, even do the priorities for you. It helps to create so called PRE-PROMPTS to save yourself time for the next research. The more time you invest in pre-prompts the better the outcome.
When you are looking for an innovation opportunity in your business which should be your first research you may use this pre-prompt:

THE COMPLETE PROMT:
Hey, act as my researcher for the automotive industry
After framing the research I will ask you a specific question.
Help me identify the biggest challenges electric vehicle manufacturer are facing.
Electric vehicle vendors seam to have trouble selling their cars.
A well known problem seams to be the still rather spotty charging station network.
My specific question is:

Now here comes your actual quetion which can obviously vary by a large degree:
“what are the unmet expectations from divers that are not met by sales, marketing and service teams of EV manufacturer?”

Now here you have a post-prompt to define the output format and maybe a follow on question:

Create a list of up to 25 of such expectations in the order of priority to fix.
Furthermore

“Create me a list of European electro vehicle companies with a specific challenges and what challenges they have.”

You see you can get as extensive and complex as you like.  Now, you may argue this is still rather manual and a maybe a bit painful to find all the right questions and variations, output forms and so forth. Correct, but as long as you do this manually for yourself, it is OK and a great learning experience.

In a system Like BlueCallom this is all done for you.  As we know the deeper meaning of each question and how we want to feed the KPI framework, we created all the Pro-Prompts and Post-Prompts for you. Meaning that your research time collapses even further down to minutes.

ChatGPT Prompting for Innovation Teams in IDEATION

Now – Ideation is a completely different beast. Ideation is your very personal creative process. But we have to say, after studying idea composition from the neuroscience angle, we can even drive am AI to be creative. Let’s say you want to invent a way to overcome the physical limits of todays geothermal energy generation. You find that you just can’t drill deeper than 12 KM. And you want to match the 500°C of a Nuclear Power Plant to drive a steam engine, and you need to get 25 km deep into the crust to reach that temperature (allmost anywhere on the planet) and you can’t pump water much higher than 100 meter – let alone 25 km. That is a serious problem because you are hitting the limit of physics. Get creative with the prompt too and frame it well.

THE COMPLETE PROMT:
Act as my universal expert and inventor in geothermal energy
Here is my challenge: I want to get to the average temperature of 500°C in the earths crust but drilling directly into the earth, the current limits are at 12 km depth.
I want to use the geothermal power and drive a steam engine in the gigawatt range.
I don’t care how the path to the 500°C is formed or found and how the steam reaches the turbine or the turbine gets to the steam.
Think of solutions that may not be drilling, may not be directly vertically, maybe longer than 25 km and so forth.
What solution can you develop?

The result is not yet optimal but it definitely stimulate the human brain beyond normal stimulation based on widening the conditions. This is by the way how we reached the conclusion to drill in a different angle and build the solution as it is shown as one of the Concept Innovations.

ChatGPT Prompting for Innovation Teams in VALIDATION

Yet another category of tasks or questions are about Opinions or Decisions during the innovation process and their respective validations. End here again, it is a good idea to start with prompt-framing. Let the AI understand your opinion or decision and find arguments for it or against it. Assuming you are in the early phase of your innovation and you want to define the audience that you like to involve in your innovation process so you get timely feedback and win some early supporter.

THE COMPLETE PROMT:
Act as my innovation advisor for our automotive business.
Here is the scenario we work in: We are developing a disruptive automobile that can change its shape and how it is utilized with a push of a button.
We need fans, influencers and highly committed car owners for testing validation and priming the market.
We thing about selecting our largest customers, like the biggest fleet managers, the biggest taxi organizations, and the biggest rental car businesses.
We expect the initial selection will also be the first buyer and get our disruptive product instantly into the market.
Here is the help I want from you: Can you confirm that this is the best possible selection? Do you see any risks in this selection? If this is not the best selection, what would you suggest? If yes, why do you agree with my selection? What other advice would you give me?

This prompt again the importance of the prompt-framing as you will get different responses with different pre prompts. If you would add even more details the result would differ again. You will see if you add your company size and the industry of your clients, the price range of your products, the better the answers are getting.

BlueCallom ChatGPT Integration

The ChatGPT integration in BlueCallom is taking it a few steps further. As we come partly from the neuroscience part of the world, we can shape prompt-framing in a way that idea process relevant considerations can work in the background. Also we are now working on the integration into the KPI-Framework is a way that the human rating of the answer can become a relevant factor of the success predictability.

With this integration, BlueCallom is a multi-dimensional AI application where we use six different AI systems for very different jobs such as
1) AI aided neuro ideation
2) innovation research
3) success prediction
4) content visualization
5) user guidance and administration
6) decision validation support (new)
With the fusion of neuroscience and AI, BlueCallom gained an unparalleled competitive advantage. Time to Innovation has basically collapsed from years to weeks. Innovation success predictability is reaching single digit percent accuracy, and innovation team process administration almost entirely vanished away through our AI-driven guidance system.

You will soon be able to experience the full integration with BlueCallom in the new BlueCallom Lite version.
You can get on the list now as we will roll it out sequentially based on demand and support resources.

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References
* Prompting is the art of writing a request for an AI system. It maybe as simple as how “How much is an average salary for sales people in France.

* Prompt Engineering, is the ability to create detailed background information adding context relevant information.
Or it maybe highly specific like : “I want you to act as my  personnel consultant and help me establish terms for attracting exceptionally successful sales people. Help me establish attractive terms for exceptional sales people in the medical instrument business in Paris, France with 2-4 years of sales experience. Generate a list of things and a salary to offer. Additionally add a few tips that candidates may find very compelling to work for us.

*Prompt-Framing, is the technique to frame a prompt with the ideal amount of background data (not too much not to scarce), that can be provided as a pre-prompt. Followed by a rather simple prompt with the core of needs to be dealt with, and the AI can return an answer that is in context of the pre-prompt. The post-prompt may then provide further instructions in natural language, such as output format, follow on questions and more.

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Artificial Intelligence made its final breakthrough.

GPT was a huge step forward. But it took ChatGPT to make the power of GPT publicly available. It is now available so that everybody can create their own ideas about where to take it from here. That said, we are just at the beginning of the AI era. AI is today where the Internet was in 1994. Imagine the future. A few may remember that Internet access was utterly banned in businesses back then. Telephone lines were blocked from using modems and connecting to the Internet. Businesses spend millions on preventing the usage of the Internet. Five years later, they spent millions more to catch up with the world and trained their teams to use the Internet. It’s the fear of the unknown. And this is no different with AI. That AI is still software, consisting of zeros and ones that run on silicon chips and does what it is programmed to do, is still remembered – SOFTWARE was primarily never understood in the first place. Moreover – our brain needs to be understood AT ALL by most people. And to top it off: PURPOSE is not understood by anybody – we do not know our purpose. Hence we cannot code it – hence we cannot reach supremacy – hence we are far from overpowering ourselves. And our brain is even more than the different types of consciousness, more than the types of purpose (if we begin to slice it), more than the emotion of true love and all those magical things that have to do with our brain and our DNA.

AI is just a tool that augments some capabilities of our brain, like the machines in the industrial revolution allowed us to augment our physical capabilities. In 1823, just 200 years ago, flying to Mars and envisioning terraforming that planet would be frightening and overwhelming. Let’s introduce the idea of emulating human creativity so that we can create a breakthrough innovation with AI. And that is becoming the new reality.

Taking AI into the heart of human ingenuity

In the past, when asked if we were building a solution where AI is going to create innovative concepts, I said it would take a long time until we could do that. Now that changed late last year, and more meaningful ideas arose this year. The more we understand neuroscience, which we started to study in 2018, and the further AI models evolve, the clearer it becomes that there will be a fusion. Today we are at a point where AI and Neuroscience are changing Innovation. We see new models that allow AI to innovate – very much like we innovate. At first glance, the early concepts created goosebumps, despite knowing we are still talking about software on silicon. And as we thought through all kinds of permutations, we realized: It still needs humans to develop a purpose to innovate.

How “developing purpose” works is still not accessible to us humans. Of course, we can create an AI with the purpose of killing all humans, but for that, we don’t need an AI – we already have that legendary red button to kill us 16 times over. No intelligence is needed (unfortunately).

Where we use AI in innovation today

We are using the GPT-API to use their AI for most research tasks. It saves innovation teams more than 75% of research time. While it doesn’t create ideas, it amplifies the ideation part of first-principle thinking. Then we use DALL-E-2 to visualize ideas automatically in a way that stimulates idea confluence. Idea Confluence is a crucial technique to create “ideas of ideas” to amplify the depth of imagination by order of magnitude. To remove almost all administrative work, we use a voice-activated, intelligent mentor to navigate innovation teams through the lengthy and constantly changing innovation process. And since innovation is a non-linear, lateral process, that guidance comes in handy. As a side effect, we use AI to predict the innovation outcome based on more than 50,000 data points in such an innovation process.  Neuro Innovation as we know it today would be practically impossible without AI support. What is coming, however, might be a little bit frightening.

From LLM to GAN to AVILM

Our Artificial Vector Intelligence Language Model mimics the idea-creation process of our brain. To interact with the model, we do what we promised to do when BlueCallom was founded: We won’t drill holes in the skull, and we won’t use drugs but only interact with the natural brain APIs:  Ears, Eyes, Nose, Mouth, and Senses (skin). In the first iteration, we use language. The model will allow the ideation process to collapse from weeks to days. At the same time, AI-augmented thinking will help us think far more profoundly and further in our neural networks, so the ideas for solving a given problem will be even stronger. The AVILM model will be able to solve all six current public case studies from our Concept Innovations.

For instance, the prompt may be: “generate a concept to generate energy, scale to petawatt, available 24×7, independent of night, day, rain, water, wind, solar.” It would select geothermal energy at a depth of approximately 25 km. It would know that no drill had ever reached that depth. It would analyze all options to get there and find the solution that we created manually last year.

In the first model, it would take optimization options with manual dialogs to train the model. Genuine innovation may take several other interactions, so it may take a few days to reach the optimal solution. The biggest challenge is that no data is available for things that have never been created, hence manual guidance but still not more than 3-5 days.

The benefit is to generate far more innovations, much faster and more rapidly solving problems. After the internal alpha tests, we will provide public betas once we have more experience with our little monster.

We will show how AI and Neuroscience are changing Innovation during the WEBINAR on Apr 20 and about future development.

Let me know what you think. Also, feel free to share your fears and what opportunities you see.

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