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Innovation Transformation Automation: Navigating the Corporate Landscape

In a world that’s rapidly advancing, where the intersection of technology and business continuously redefines industries, it’s imperative for corporate leaders to stay informed and adept. For those at the helm of management in large companies and for AI technologists, understanding the nuances of Innovation, Transformation, and Automation is not just a luxury—it’s a necessity.

1) The Core Differences: Innovation, Transformation, and Automation

  • Innovation can be visualized as the heartbeat of any forward-thinking organization. It’s the act of introducing something novel—be it a groundbreaking product, an ingenious process, or a revolutionary idea. The essence of innovation lies not just in conception but in execution. It’s about fresh thinking that manifests tangible value. But innovation isn’t a mere sporadic burst of creativity; it’s a continual commitment to pushing boundaries.
  • Transformation, on the other hand, is the phoenix of the corporate world. It signifies a metamorphic shift in how a company operates. This could encompass a change in its foundational business model, core operations, or even its cultural ethos. Unlike mere change, which is incremental, transformation is holistic, giving birth to an entirely renewed entity.
  • Automation is the application of technology designed to perform tasks with minimal human touchpoints. If innovation is the heart and transformation the soul, automation is the muscle of modern businesses. It’s about leveraging technology to its full potential, ensuring efficiency, consistency, and often, accuracy that the human hand might falter in.

2) Living Examples: Demonstrating the Power of Three

  • When we recall Innovation, Apple’s introduction of the iPhone is a luminary. Not just a new product, the iPhone changed how consumers thought about phones. It wasn’t just a device for calls—it became an integral part of our daily lives, blending communication, entertainment, and work.
  • Transformation has its cautionary tales, with Blockbuster being a prominent one. As streaming platforms rose to dominance, Blockbuster, once a giant in its realm, attempted to transition from physical stores to online content. Their journey underscores that transformation, while crucial, isn’t a guaranteed success but a strategic necessity.
  • And for Automation, car manufacturing offers a crisp illustration. The adoption of robotic arms in assembly lines has not only sped up production but also introduced a level of precision previously unattainable. Similarly, in the digital space, chatbots attending to customer queries at all hours exemplify automation’s prowess.

3) The Art of Choosing: When to Innovate, Transform, or Automate

  • Innovation is the tool of choice when exploring uncharted territories. Companies aiming to capture a new market segment, address a previously unidentified need, or offer a unique solution often lean on innovation. It’s about building a bridge where there’s been no path before.
  • Transformation is the answer when there’s a seismic shift in the industry landscape. When external factors—technological disruptions, changing consumer behaviors, or geopolitical shifts—demand that companies reinvent themselves, transformation becomes inevitable. It’s about ensuring the ship not only stays afloat but can navigate the new waters adeptly.
  • Automation becomes the focus when efficiency is the goal. Whether it’s to reduce overhead costs, eliminate repetitive tasks, or ensure a consistent quality of output, automation streamlines processes, making them more predictable and scalable.

4) Weighing the Economics and Strategy

  • Investing in Innovation can be a gamble. The R&D costs, market research, and prototype testing demand substantial financial commitments. However, successful innovations often yield exponential returns, granting companies not just revenue but a competitive edge.
  • Transformation is both a financial and cultural investment. Beyond capital allocation for new tools or business models, it demands a shift in mindset across the organization. The payoff, though, is longevity and continued relevance in an evolving marketplace.
  • Automation, while demanding initial technological investments, promises long-term savings. Reducing manual labor, minimizing errors, and accelerating processes, it ensures companies can deliver more for less.

5) Crafting and Executing: From Ideas to Reality

  • Innovation thrives in environments that nurture creativity. Beyond financial backing, it requires a cultural acceptance of risk, iterative testing, and a feedback-rich ecosystem. It’s a balance of brainstorming sessions, prototyping labs, and market tests.
  • Transformation is a marathon. It necessitates buy-in from all organizational levels, from the C-suite to the front-line employees. Often, external consultants, familiar with the transformation journey, can offer valuable insights. It’s a mix of strategy meetings, training sessions, and constant communication.
  • For Automation, technical prowess is paramount. Integrating new systems with legacy ones, ensuring staff training for automated tools, and maintaining these systems are integral. It’s about tech assessments, integration blueprints, and regular software updates.

Conclusion

As we stand at the cusp of a future molded by AI, understanding the trinity of Innovation, Transformation, and Automation becomes crucial. For management and AI technologists, it’s not about choosing one over the other but discerning which to leverage when, ensuring that companies are not just surviving but thriving in the corporate landscape of tomorrow.

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Ambassador Role Model Nepal

There is a lot we can learn from Nepal. The very beautiful and sometimes considered mystic country could not have more orthogonal dimensions. Nepal is at the very low end of the GDP list, is unfortunately high up on the list of “perceived corruption”, is a nation with the one of the most kindest people on earth, has exceptional talents, a still under developed infrastructure, is still dependant on donations from foreign countries, yet some extraordinarily ambitious people to turn the nation from a “receiver” nation into a fast emerging nation on the way to become a “giver” nation. When such a country, with a new generation of sheer infinite determination can organise to breed talents working on globally latest technology such as Artificial Intelligence, with goal to turn the nation to prosperity – we must ask shouldn’t that be possible in other countries too. We also must wonder if the combination of a new agile government, highly engaged academia, highly motivated entrepreneurs, all working together – is a superior model of the future? Or will the model of a public being permanently on confrontation course with their government, ego driven groups with nothing but steering up the nation with horror scenarios for their own good and media loving to confuse information consumer for the sake of popularity be the winner of the future?

Khem Lakai – Nepal

While we, the World Innovations Forum, has pretty much all ducks in the row here in Switzerland, a very active community in San Francisco, where it all started, and a very good start recently in Bosnia, great energy in South Korea, Vietnam, Germany, Macedonia, Nigeria, and other countries, our current Role Model is Nepal. Khem Lakai our Ambassador, had by far overachieved our wildest dreams. After our first Meetup in 2018, and a good exchange during the year, Khem understood, it was important to get top technology created in Nepal. Since natural resources are limited and industrial production is not too well developed yet, competition in other countries is fierce, he decided to help stimulate tech development. Together with Ranjan Mishran, a Nepali who is studying at ETH in Zurich they inspired a team of PhDs from Zürich and other Universities to come to Nepal. The Swiss Embassy in Nepal immediately recognized the importance and supported his actions.

Kathmandu, Nepal

In nearly no time, students in Nepal are being trained and built an AI systems and have been stimulated for Entrepreneurship. With yet another group of Nepali tech enthusiast in diaspora, lead by Prof. Bishesh Khanal who decided to quit his dream job in London to move back to Kathmandu and help Nepal move forward with other very successfully tech professionals and experts in the field of AI. Khem worked closely with various entrepreneurial enthusiasts in the nation, co-sponsored national events with Nepal Tourism Board and mentored youth in politics from all different political parties to raise awareness for a “visionary leadership”:  Nepal is to change the narrative of poor and sorry nation to a successful strong nation.

 

Premier Minister Khadga Prasad Oli of Nepal with World Innovations Forum Chairman Axel Schultze.

A few months later they invited Axel to speak with the Prime Minister Oli about the World Innovations Forum’s overall plans and also having talks with their Finance Minister Dr. Khatiwada. The power play continues this year with a first International Investors Summit in Nepal. Now Axel is preparing to attract international startup investors from the US, Germany, UK, Switzerland and maybe a few other countries to Nepal. While the country is still perceived as a rather corrupt nation, we see already Nepali Finance Minister starting to bring the legal framework in alignment with International expectations. The extraordinary journey is just in the beginning.

Khem Lakai, the World Innovations Forum Ambassador, together with his connections and a very ambitious country is making the sheer impossible a reality. It’s the concerted effort with an exceptional leadership that made this work. It was only a spark of inspiration from the World Innovations Forum,  yet the highly focused, ambitious and self determined Khem Lakai did what he felt is right for Nepal, connected with likeminded people and relentlessly executed. It’s that mindset and the understanding what really makes sense for the larger part of a country that moves mountains. In the meantime a new innovation lab is in the making. Also a collaboration between another Swiss university with a Nepali University is considered to create an exchange between some top Nepali talents and Swiss talents to also shorten the distance between cultures.

Its the right time for the right action with the right people that makes a change possible. This is the spirit the World Innovations Forum is trying to embrace. Our most sincere THANK YOU to Khem and his team of equally ambitious team of exceptional people like Ranjan Mishran, Prof. Bishesh Khanal and many others to build this World Innovations Forum poster child.

Even though Khem is the prototype of a self starter, let us inspire all of you to do what is best for your country as every country is in a different situation. But we are all one world – together.

@MaritaR

 

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Demystifying Artificial Intelligence

I see thousands of horror stories and get hundreds of fearful questions like will AI be harmful to humans, will “they” take over the world. People see them already as fully developed beings and thousands of times superior over humans. There is an interesting psychological analysis about that – but first let me stop this absurd craziness.

WHAT IS ARTIFICIAL INTELLIGENCE?

First and foremost AI is a piece of software. It is typically written in very well known and for decades widely used programming languages such as C, C++ or Python. To make the software do what is smarter than just “if this happens then do that” software developer are using smart algorithms – mathematics. Another technique that is used is so called “Neural Networks” – but also that is pure software. The neural networks is a mechanism that is seen in our brain handling all kinds of filtering, sorting and decision providing mechanisms. So all in all, AI is software, math and libraries of techniques.

Instead of keyboard and mouse, so called “Natural language Processing” tools or in layman’s terms: speech recognition tools are used.  So we can talk to the computer and it will talk back by using speech synthesiser. And since we are used to use web cams, electric thermometers, GPS and stuff, we can attach these to the computer as well.

Now the combination of computer, software and all kind of sensors, the “machine” looks much smarter than before. But hey, it is still a machine, doing what the software we wrote, is dictating it to do.

Another thing, which started the avalanche of hysteria and craziness, is the idea that in the combination of smart algorithms and neural networks, we can actually program the machine to “learn”. The only magic in this is that we humans have an enormous respect for “learning”. The machine however learns VERY differently than we. Give the machine 100,000 images of dogs and it will learn how a dog looks like. If we now add a dog in 100,000 other pictures where also cats and other animals are in, the likelyhood to recognize the pattern of a dog is pretty high. In contrast to a human, my daughter took about 20 to 30 impressions of a dog to “learn” what a dog is. She also learned that a cat is a similar animal but not quite the same.

Why is AI such a big deal

Well, you could give an AI system the construction plan of a car. Then ask it to create an optimal body, that is as light as possible, as stiff as possible, has a dynamic material behavior in case of a crash and considerable ,ess expensive.
The AI system could instead of only 100,000 views take 10 Million iterations of each component and come back with a design where the car is only 100 KG instead of one ton, has a network style body instead of full material and using all kinds of stiffness measures that it is stronger and at the same time lighter. With less material and for instance only steel at certain point, carbon in others and so forth it would eventually cost 50%, weighs only one 10th and is much safer. Artificial INtelligence can be programmed to do things like that basically anywhere. Making enormously precise Weather Forecasts, analyze stock exchange behavior of thousands of companies over the past 100 years and  the behavior of investors by the minute to predict much better value development. It can help analyze illness, make more accurate and more neutral decisions as a judge and so forth.

If it is so cool – why don’t we do all of this now?

Well – AI is still in development. Approximately 1 million engineers in approximately 100 countries work independently on all kinds of solutions. Mathematicians join the software developers and vice versa. However it is by far not as easy as many people think. The most advanced development right now is autonomous cars. Invented originally in Germany in 1992, where the first autonomous car drove 2,000 KM from southern Germany to Scandinavia and back, all in autonomous mode. Autonomous cars may then take 20 years to get all the kinks out and be safe enough to get on the road. Politicians go back and forth whether they should support it or not, the public is in hysteria to say the least and there is a lot of uncertainty about how AI could be turned into weapons.

What risk remains with AI?

Let’s assume for a moment, we would not have automobiles today. Now somebody would come and suggest we build cars and street, but also need to take in consideration that every year more than one million people will get killed and 20 – 50 million insured. Would we allow that anybody is building those killer machines? Never ever I guess. But the CAR is not the killer it is the human that drives the car. Like in all other ares, the human that uses the gun, the human that uses the knife, the human that drives the tank. Yes, AI and Robots could be build to go to war, kill the opposing country and possibly wipe out a nation. But it is not the robot that magically makes that decision – it is the human that designs it. And as seen in 1945 in Hiroshima, we could do that already in the last century.

On the other hand, we have 340 Million occupational deadly accidents a year. How about accelerating the AI and robotics development to get machines into those jobs and safe 340 Million humans each year. This is more than people dying at wars and accidents all together.

Yes, there are risks that ill-minded people take this technology as a weapon against others. We saw this with Social Media, the Internet, any type of technology in the past.  and we need to do everything to prevent them. But those machines build from plastic, silicon and metal will not magically turn into living things with a purpose we didn’t program and wipe out all humans or stuff like that. Those visions – even coming from people like Steven Hawkins – are based in a huge deficiency of understanding what humans really are and that our “intelligence” is not more and not less to our brain, than  the muscles are to our body. Today we can lift thousands of times more with our cranes than with our muscles. Soon we will be able to think through constructions or algorithms thousand times more complex than we can calculate with our brain. But one thing have cranes and AI in common: Humans have developed them and have the interest and responsibility to make them as safe as any way possible.

Autonomous Machines, driven by Artificial Intelligence, are no living beings with a purpose to kill us. They are machines with a purpose WE GIVE THEM. And if that purpose is killing, then it is still us who made it that way. It is not something THEY CAN DO unless we allow it to go that way.

 

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20 Animal Inspired Robots

Just saw this on LinkedIn :)
amazing progress


 

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Autonomous Machines – where are we today? Who may advance the most?

We came a long way in the last 1 and 1/2 years since this amazing video from Maurice Conti was recorded. Artificial Intelligence made huge progress. The hardware however is actually much behind the hopes. Autonomous machines like robots lack the skills to move like humans or animals. Other autonomous machines like cars or ships still lack the all encompassing sensor technology to be truly safe. Autonomous earth bound autonomous machines like chat bots and game computing however show great evolution in their hardware technology.

The software advances are more significant: AlphaGoZero was beating AlphaGo 100 to 0 – meaning the amazing AlphaGo program, which was beating the number one Go player in the world 4:1 was now beaten 100:0 by its next generation AM. The critical part of that story is that it was able to learn everything on its own. Chinese President Xi Jinpen announced his goal to be AI leader by 2030. German Economy & Science departments expressed their desire to make AI made in Germany a globally known quality brand for AI by 2030. Approximately 1.000 times as many regular people on the street became aware of a possible social shift in mankind. grander than anything we experienced or even imagined before. Fear however is spreading at almost alarming rate now – not a place we should be in. But it’s only indirectly the fear from machines. Deep inside our subconsciousness it is the fear of our own miss doing. Not our imperfection but doing things that are simply not right – and doing so in full awareness. Outside the technology and opportunity realm, we began to learn more from the AI development about ourselves than ever before.

How important is technology for AI?

In my work on that subject, I recently had an interesting thought. Mainly when I heard that the two vocal contender China and Germany longing for attention, I asked myself what they have to bring to the party. Today’s AI leader is clearly from California. But is technology really at the core of AI? Yes, we need powerful processors and lots of memory. But AI is no longer really about technology per see. At the core of any AI system are the algorithms, complex mathematical constructs that can describe a behavior, a process, and create results for possible more algorithms. Both Germans and Chinese are pretty good at that. Further more AI is equally dependent (today) on neural network skills. A structure where lots of data get feed in, and spill out results or even directives to other algorithms. Decision making procedures are other skills AI need to employ to create meaningful results. All these skills can be found widely in Germany and Switzerland.

Sooner or later all engineers will notice that no matter how much computing power they have it will never be enough. The game of GO is a great example. The number of possible moves from start to finish is said to exceed the number of atoms in the observable universe. Any AM (Autonomous Machine) will need to calculate its best result or step to a result based on the most probable right step, accepting it may not be the best. AMs need to be FAILURE TOLERANT. With that, an AI system actually gets closer to us humans. I’ve met AI researcher in Taiwan working especially on that part of the process.

But here comes – by far – the most challenging part of AI development that is actually useful for people: What questions do we have to have an AM answer it profoundly and efficient. To make AMs do repeatable jobs like in a production company, or repeatably answer questions, even millions of different questions, from customers in a pre-sales department, or answer questions from patience regarding their possible illness. that is not so complicated. But to answer the question: “How can we help politicians, make the right decisions that are in the interest of the population – and – taking long term effects into consideration?”. Most of us do not (yet) have a firm answer. Most of us, including me can’t even articulate that question comprehensive enough for any system or human to trigger the right solution finding process. Now that is an area where Central Europeans, including Germans are actually pretty good at – maybe better than others.

Artificial Intelligence is more about asking intelligent questions to get intelligent results that putting together fine technology.

While I can continue with a few more aspects of meaningful requirements for a great AM solution, it became very apparent to me that technology keeps taking less and less a center stage position and other, social, psychological, behavioral aspects move rapidly forward. It also daunted on my that there will probably not be a single country doing it all or being the leader – it is a global development where the most open minded and smartest people can jointly produce unprecedented results. Brut force leadership by order of a human leader is most certainly failing – even with 10 billion people. But some of them will contribute amazing solutions to the whole.

Leadership Framework

The World Innovations Forum put a leadership framework together to help all countries to get started and advance. Getting everybody involved makes a lot of sense when it comes to, what is said by some, the last innovation humans will do. Most importantly collaboratively developing and agreeing on a common set of rules will make this development much safer. But maybe we need an AI system to guide us through a process that may otherwise be too much driven by ego and self interest.

What’s next

The development of chat bots is evolving rapidly. From clunky little experiments a few years ago, we see great progress and probably have a highly sufficient “chat bot” by 2022 that is so strong and so versatile that it may replace more than 100 million call center employees with the only replacement of maybe a million people training and advancing those bots. By that time governments will have no other choice to tax the productivity of any AM no matter where those machines are hosted and no matter who owns them. The tax income then can be used to start the concept of a base income for those who won’t find work any soon. A whole taxation model will be available in the new book “World with no work“. By 2025 until 2028 we may be in a position that super advanced chat bots can replace many knowledge base systems including Medical Doctors, Lawyers, Bankers, Insurance Brokers, Inside sales people, cashiers and so forth. This may cause an even bigger avalanche of unemployment that my easily hit over half a billion people world wide. Only several million will be needed to review the medical suggestion, the investment advice etc. But will no longer need a time consuming deep dive. By 2030 robotics may have advance to be much more agile. My ultimate test for a robot would be: Successfully perform in three disciplines: 1) Successfully play soccer in one of the top leagues. 2) Climb Eiger North Face or any similar mountain and return safely to base camp. 3) Dance tango with one of the world class dancers (it takes two to tango). “It takes two to tango” has an inhered interesting aspect: unlike most dances, Tango is lead by both dancers with an unspoken intuition. That will kick start another avalanche of AMs performing tasks that have been conducted by well educated humans. With a constantly improved UBI (Unconditional Base Income) by now it becomes actually a desire to replace as many human jobs as possible and give humans the freedom to pick and chose activities they really love and be so good and social in it that there is no advantage to have robots. There is no advantage to mass produce robots that produce art better than humans can do. Art has no better or worst – Art is a human expression not a robotic mass produced expression, even if every piece would be individual.

Autonomous Humans

By 2040 or 2050, humans will rationalize that during the time of the industrialization, they lost their autonomy. No job – no happy live. If you can’t get into a company where somebody is telling you what to do, you won’t get the reward in cash that you need to survive. Or if you do not perform what is expected from you, you get fired. Not today – but in 100 years we will compare that life with modern slavery. How could it be that you – a free person – has only a few hours a day actually free. At least, unlike in the 1800’s you no longer get beaten up or cut off your hand, but still you call it yourself treadmill, you are very happy if it is 5 pm, you look forward to vacation, you have no or not enough time to do what you really would love to do, you may not even have your dream job, you can’t follow your intuition but the job rules and so forth.

However exactly that ‘YOU’, is the one who helped build a world that eventually transform to a much more liberal life, less dangerous tasks and way more pleasant. So nobody following the rules today is/was stupid. But look for the possible outcome in 30 years from now. Learn everything possible including risks and opportunities about AI and AM and only if you can make an educated decision, judge it – as an hopefully autonomous human.

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AI Leadership Framework For Countries

THIS IS WORK IN PROGRESS

Many countries explore ways to get not disconnected from the AI development even though the risks and opportunities of AI are highly controversial.

Since several of us are working in the Artificial Intelligence space for quite a while, we decided to put up an initial framework for country leaders. We believe that only a program developed in concert with other stakeholder has a good chance to be accepted by all.

It is in collaboration with our ambassadors in roughly 20 countries. More helping hands are very welcome. We will be updating this program on the go – leaving the previous versions as blog posts here. Theoretic AI is developing rather quickly and so should be this framework.

AI Leadership Framework

The “AI leadership framework” is a project in development. We are working with a large number of great minds from over 20 countries to postulate a framework that can be used as is, or as a base for a country’s own AI Leadership Strategy. While we see the risks that AI has, we see the grander opportunity for humanity. And if nothing at all, in the next 50 years we will learn more about ourselves than during the entire lifespan of humanity.

1) Data Awareness

Data is a new natural resource for the highly developed world. What was Gold, Coal, Oil, even healthy soil and so forth in the past,  is data in our immediate future. There is an abundance of data. The key is to intelligently harvest the overwhelming amount of available data. Being aware of the fact, that we all are creating new data, billions of data every single day, is the first step to create policies, infrastructure to protect – but also motivation to share those date. Like in previous technology developments, the US has been in a leadership position early on. Data power houses like Google, Amazon, Facebook, the credit card organizations and telecommunication companies, already sit on data like no other country.
Country leaders should understand: Data are generated in almost every country, including emerging countries and even some sub emerging countries. Not being able to use those data in a secure and privacy protected way is a major obstacle in a countries development. Natural resources just got an additional position in their list.

2) Access to data

The more data can be assessed the better the results from AI systems. In particular in the early days of any AI development, it is critical to get access to an enormous amount of data. Enormous means several hundred thousand records about the same topic. Over regulated and fearful societies and those with fear driven privacy campaigns are clearly in a disadvantages position. Amazon has access to trillions of dollars worth of shopping from past years. Together with data usage from their AWS business unit Amazon is one of the top player globally. Google sits on quadrillion of data points from all the searches and knows what people look for, why, what problems they have, what products they search,what illness they have and so forth. Facebook, unlike the other two has much more social interaction related data. The recent collaboration with credit card institutions provide Facebook a hot mix of social and commercial data and relations. Of course privacy is an issue but not using the data is like having hectares of apple trees but letting the apples rotten in the ground.
Country leaders should recognize that blocking data access due to the inability to protect the owner’s privacy of those data brings those countries behind. Retraining a population that ‘it is OK now’ is a daunting task.

3) Engineering

AI is no longer an IT discipline. AI requires top level mathematical knowledge, understanding of neural networks, understanding of reasoning and decision making processes, understanding in cognitive behavior, awareness know how, and much more. Image and speech recognition skills are simply a given. The vast majority looks at AI as a software code that is written by humans and can do at best as well as the programmer. That an AI system can not only perform computational processes but also build intelligent correlations, make then decisions and outperform humans in almost every predictable project is foreign to most of the postulation. The algorithms (mathematical procedures) allow AI systems to learn from hundreds of thousands of situations, then construct millions of situations similar to those they learned from and have a richer set of “experience” than any human ever being able to accumulate. While this is good – we need to understand the implications.
Country leaders should know that no longer only engineers are needed to develop leading AI applications but also Social Scientists,  Biologists, Mathematician, human behavior experts and related skills.

4) General Education

In past major development shifts, the education of what’s happening was more or less based on the effort of the businesses who produced and marketed those new technologies. However the possible impact is too significant to only hope the education will work out. If 50% of jobs will be irreplaceable eliminated, we need concepts and education how who this will not turn out as a catastrophe. We need to explore options in advance and educate those who have been educated to look for work and do what others tell them to do. what opportunities are out there in this new world. For the past 300,000 years humans have been taking care of themselves, have been rather autonomous and been creative to survive. In the past 200 years that has changed. The main job was to look for work and just do it. We are in a way going back to more autonomy and self determination. While this is an amazing and positive development, for many people it is on open void they may not be able to fill by themselves. Education and guidance is a key aspect of leadership – always was.
Country leader should seriously consider a greater makeover in their respective education systems. Mechanical learning of Physics, Math, Chemistry, Biology, History, Languages, is no longer enough. And as our knowledge base now doubles every year and the learning capacity of the young already crossed the limits, we need to consider actually reducing the amount of the content in the core classes and add data technology, society and political rights and responsibilities as these topics require a better understanding of everybody.

5) Language Empowerment

The world’s information is by and large stored in English language. Even the use of the term ‘Artificial Intelligence’ is important to be held in English and not in a local language. One of the key success aspects of AI is the openness and willingness to cooperate globally – that means by default in English. Leadership by ‘closed shop’, and keeping everything close to ones chest and secretive will never lead to superiority.
Country leaders should seriously consider reviewing the language learning in each country. Even though it is part of the general education, it has a very special position in the AI development. No longer it is only about the globally universal language of code writing, there is also the new language of system interaction. Already today AI components outperform any language on earth with more than 1 to 100 relative to English. Roughly 200 French Language ‘Skills” (applications) or close to 3,000 German skills versus more than 10,000 English skills.

6) Culture & Failure Tolerance

Whatever a team is creating, it is critical to get the early prototype rapidly into the market and work with massive amounts of actions. AI cannot be only tested in a lab – it needs to be tested in the market. The European or Asian perfectionism is counterproductive to AI development and will automatically make those actors fall behind. Google’s AI project that was used to learn about battery usage in Android smart phones moved within six months from concept to being used globally. We can learn only from errors. We will not learn from a perfectly working solutions. What sounds stupid to many engineers has been proven right over and over again. The motto Fail and fail fast is even more important in AI development.
Country leaders may want to consider driving an initiative that makes making mistakes an act of learning and progress rather than an act of failing. Chinese president Xi Jinping addressed Chinese entrepreneurs early 2018: ‘Making mistakes is the best way to learn fast’.

7) Transparency

Due to the competitive nature of any new technology, we most likely will never be able to find out who is working on what. Our brains are still the most private part of our lives – and hopefully will be forever. Yet, we should develop a work ethic that calls for voluntary transparency on the Matter of AI development. Now, recent incidents demonstrated that a business may not trust its own government because of its sheer power. A government should not even attempt to play a controlling role in the transparency question. And since trust is one of the key issues in that, maybe a system like we know from the blockchain development maybe a future option. Another reason why we should keep governments out of this role is the lack of trust among governments themselves. The current suggestion is that AI scientists select a consortium of trusted, anonymous third parties who get tasked to oversee the development as such and analyses the potential risks – with no authority power.
Country leaders may want to consider endorsing such an engagement, maybe send an observer but not ‘control’ it in any aspect. The control should instead lay in relevant and meaningful AI safety policies and law enforcement activities.

8) Taxation

There is a good potential that Autonomous Machines (AI driven systems) could wipe out 50% of the ordinary jobs by 2100 (plus/minus 50 years). Any given society would not recover from such a situation without well thought out planning. One concept maybe that each robot may be charged with a tax equal to one employee. That “AM-Tax” can then be used to fill a fund for unconditional base income or similar system. While the company would still have to purchase such a machine, it can quickly amortize the investment with this artificial employee not taking vacation, not needing social extras and possibly can simply rented as needed. Several scenarios can be found in “World with no work”.
Country leaders should be way ahead of time working on models for their respective society. That includes considering the competition from outside their country. The AM-Tax might be on conjunction with the production output (revenue) or operative savings (cost) they produce or save.

9) AI / AM imports

When importing AI from other countries we need to be aware that this may inherit a certain danger. Those machines may not comply with a country’s Safety & Privacy roles. Data maybe abused or used illegally and more. Importing such machines is a similar subject like importing weapons.
Country leaders should consider to establish early on AM (Autonomous Machine) Import rules and most importantly taxes in line with their overall AI or AM taxation.

10) Safety & Privacy Policies

Another key aspect of the leadership framework is the existence of the “Safety Trio”:
1) Privacy & Data Policies
2) Criminal Acts Policy
3) Human Protection Policies

10.1) Privacy & Data policies
need to be established to not only protect the general population from misuse of data but also education about the consequences of not providing data. Data as such needs to be structured beyond the act of privacy protection but also the protection of data, that belong to the creator of those data. Yet, it needs to be understood that data can either belong to one person or legal entity in its entirety or belong to a whole group pf people or entities. The complexity and permutation of data needs to be carefully explored on the go.

10.2) Criminal Acts Policy
Most likely the act of hacking systems and stealing data, corrupting data or systems or destroying either data, systems or network infrastructure will want to be set as a serious criminal act. As systems get more sophisticated they also can cause more damage than in the past and can bring people and a whole country into serious trouble. AI leadership needs to demonstrate its sensitivity for protection while at the same time its openness for sharing data. As such, hacking needs to be elevated to a serious criminal act with substantial penalties. The criminals act policy should also expanded into political treaties where country leaders agree on respecting the criminal acts policies of each other.

10.3) Human Protection Policy
There is s great level of fear, that AI based Robots, so called autonomous machines, may be at risk of harming humans. Right or wrong, AI leadership requires responsiveness and offers a solution. A possible method is to legally enforce that every autonomous machine will need to have a mechanism to be remotely shut off by especially selected authorities and/or mechanisms. A structure like the domain name service where systems are distributed around the globe may also be applicable to distribute the shut of mechanism. Robots without such a mechanism maybe considered illegal and by order of the law destroyed and the creator and owner penalized. The instantiation of the ‘Criminal Acts Policy’ is in particular important for this ‘Human Protection Policy’.

 

 

SUMMARY

As stated in the beginning, the AI Leadership Framework is in an early stage and far from being complete. But in the interest of transparency also of our work, we wanted to share where we are at this stage and what we are working on. Suggestions may change into very different directions based on inputs and the work we are doing going forward.

 

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Why today’s human minds are incapable to outperform AI

Today we may debate whether or not a robot will ever be superior to a human. Superiority has many angels. And a robot may not catch all of them. But in the basic work life, it is different. And maybe one robot may not be as good as a human – but a whole range of networked Artificial Intelligence-based robots will outperform us in almost any segment of work on all levels and all industries !!!

 

Our single biggest disadvantage

The vast majority of humans, still today, keep knowledge close to their chest. Sharing of knowledge, experience, and mistakes is not our biggest strength. Sitting in a corner, thinking through the permutations of what happens with a machine in certain circumstances – is our biggest strength. But we know that our brain capacity is not unlimited. As long as we continue to share only if really necessary, think for ourselves, hope we earn special attention for our knowledge – we are in danger that all our jobs get eradicated before we adapt to newer better behavior. Our physical limitations are too weak to stand up against a series of highly developed AI networked technology – whatever we call it.

Cultural Advancement

We are what we got introduced to by our cultural frameworks. Our parents teach us to be humble, not to share our experience unless it is necessary, to experiment for ourselves until we are sure that our experiments are successful. We are conditioned to not talk about things that are still uncertain. Our communication prison is huge. Some cultures however advanced already. And the most interesting thing is that it was actually that very advancement that brought the AI / Robotics technology to life – Silicon Valley. Now some may argue it was elsewhere and so and so already had developed that first version of AI years ago. Well – so sorry to say that, an innovation has absolutely no value unless it is brought to a broader market. “The initial value if any innovation is zero”. Without cultural advancement, we will maintain that widening gap between developed and emerging countries. As long as emerging countries do not embrace more openness, a culture where failure is not just OK but actually good and a key part of learning – the country will remain to be an emerging country.

Artificial Intelligence, biggest driver for human advancements

Humans have one extraordinary ability: humans can adapt to new situations within one and the same generation. No other life form can do that. Big Data is giving businesses who leverage big data a huge advantage over others because they simply know better and faster what is going on. If we learn that networked AI systems will be able to tap into those data and create analysis, able to make decisions and derive strategies from the results, They will be ahead of us and we will essentially do what they suggest. We can’t even verify in time so we simply go ahead and do it. Yes it’s still a tool – but we do what the tool is telling us what to do without even being able to debate it. But we will learn one thing: if we connect our brains we get a whole new edge – maybe beyond our own imagination.

Experiment at Society3 World Innovations Forum

Imagine we do what these AI networks do and network our brains, very simply on a daily base by sharing, communication, analyzing our own mistakes and come to new conclusions every day? The collective intellect when really in action has unknown and incomprehensible reserves. The least we can do is to explore them. And it is almost for free. We only have through some of the old cultural remains overboard. At Society3 this is exactly what is going on right now. Society3 is building a digital layer across the globe that exists to connect people and their ideas, challenges, questions and answers together. http://society3.com We are not here to win a competition with AI-based robots but simply bring human entrepreneurs to a level that has never seen before.

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