Blockchain: From Utopian to Practical

Safe, Scalable, Cost-effective and Now

Many organizations are testing blockchain solutions today as demonstrations and trials, while relatively few “blockchain compliant” systems are in mass scale production.  The reason for this is understandable.  Some firms hold back because of the mass complexity and development costs of re-writing systems that already work fine.  Others are on the fence because they don’t know the policy implication and even what might not even be legal in the near future.

When we launched our first Blockchain Lab projects four years ago, we took the position that we intended to separate hype from reality.  After looking at many possible applications and the underlying technology, we have found methods to overcome issues in practical and cost effective methods by being smart about how to implement the system.  I’m writing this today, because, I’d like to invite a small number of firms to try this approach with us – we welcome you to contact us if you have been thinking about blockchain but can’t see a clear path to safe, scalable deployment

If you break down (unlink) the blockchain, it turns out that there are a specific set of benefits:

  1. Immutability – being able to write something that will not be erased
  2. Transactions – being able to record a transfer of an asset
  3. Identity – being able to link what is written to a person or machine with a particular key
  4. Resilience via distribution – you can eliminate many nodes or ledger without loss
  5. Smart contracts – tamper proof automated transitions per a policy

What is interesting to note is that every one of these items above can be done without employing a complex blockchain-based system. It is just that the infrastructure to do all of this in one neat, well-defined, scalable, secure, de facto-standard approach has not been developed yet.  One option is to wait a decade until the infrastructure is ready and tested.  In fact, many ventures have proposed solutions to the current trilemma of not being distributed, secure, and scalable.  We don’t have that utopian solution yet, but we likely will eventually have it all in one platform.

However, what we have found in our work at the Berkeley Blockchain Lab is that it is practical and possible to develop systems and applications that have all of these capabilities today by being smarter about the solution design and by using a better mix of pre-existing components.  It is true, we don’t even need to throw out the existing code base that we already have.

If you think about it, we have automated contracts that run every day in code.  If you don’t pay a credit card bill, you get a late fee.  That “is” a smart contract.  It is just not distributed and it is not tamper proof – meaning that someone could change the rules or the amount of the fee and you may not be able to tell.

But suppose it is possible to add resilience, tamper-proofing and blockchain compliance as a feature without significantly changing the existing code base, redesigning and re-testing, and adding additional concerns about policy. Instead, we can add the blockchain as an incremental capability.

We have found that this is possible and practical to change the blockchain implementation complexity by an order of magnitude in the process.  Definitely let us know, if you would like to collaborate on a real-life, practical, scalable, policy-safe, blockchain compliant application that actually makes sense to deploy.


The post Blockchain: From Utopian to Practical

appeared first on UC Berkeley Sutardja Center.

Building foundational blocks with Blockchain at Berkeley

The Sutardja Center for Entrepreneurship & Technology congratulates the Blockchain at Berkeley student-organization for an outstanding year of contributions to the Berkeley blockchain ecosystem — see their State of the Ledger 2018!

UC Berkeley, the Sutardja Center, and SCET students have been involved in blockchain since 2014 when professional students in SCET’s Engineering Leadership Professional Program authored a paper highlighting Bitcoin’s potential impact on banking. Since that time, Blockchain at Berkeley, which sprung out of student activity at SCET and works by affiliated SCET faculty, has been working to further the development of this revolutionary technology at Berkeley.

See more on our Blockchain X-Lab site and at Blockchain at Berkeley.

Blockchain at Berkeley students

The post Building foundational blocks with Blockchain at Berkeley appeared first on UC Berkeley Sutardja Center.

Now Available: Introduction to Mindset and Behavior for Innovation (video)

Which mindsets and behaviors do successful entrepreneurs typically have? How can entrepreneurs learn and practice these mindsets and behaviors to become more innovative?

Presented by Ikhlaq Sidhu, director and chief scientist of the Sutardja Center for Entrepreneurship & Technology (SCET). This 1 hour lecture is a helpful resource for any instructor, student, innovator, or entrepreneur. This video was developed to help students of all types understand the mindsets for innovation and entrepreneurship as well as techniques and simple exercises to expand comfort zones, overcome challenges, and increase innovation behaviors. Now available at no cost on our public X at Berkeley channel.


The post Now Available: Introduction to Mindset and Behavior for Innovation (video) appeared first on UC Berkeley Sutardja Center.

Blockchain arrives on college campuses

The University of California, Berkeley’s Sutardja Center for Entrepreneurship & Technology (SCET) is one of the first institutions to offer university courses on blockchain technology. Specifically, SCET’s Blockchain Lab facilitates collaboration between new ventures, industry scholars, and academics to evaluate, validate, and execute innovative blockchain projects.

“Academics feel a lot of pressure to maintain their status as ‘world-class experts’ in some narrowly defined field,” said Po Chi Wu, visiting professor at UC Berkeley and instructor of Blockchain Lab. “Just keeping up with the advances in their field is challenging enough and demands all their time and energy.”

Ikhlaq Sidhu, founding director and chief scientist of the Sutardja Center, suggests the hesitation of universities in providing blockchain related courses stems from their uncertainty in differentiating short-lived trends emerging technology trends from long-lasting ones.

“We expect a wave of blockchain solutions that will solve a new set of social issues to disrupt the current world order,” said Sidhu and Alexander Fred-Ojala, research director of Data-X, “Not teaching about blockchain would be equivalent to ignoring internet technology when it emerged 25 years ago.”

For more information on Sidhu and Fred-Ojala’s take on the future of blockchain, click here.

Read the whole story at Wired

The post Blockchain arrives on college campuses appeared first on UC Berkeley Sutardja Center.

The Applied EQ Project: EQ-Driven Technology Disruption

Written by Ikhlaq Sidhu UC Berkeley and Paris del E’traz, IE Business School.

We have identified two types of mindsets that exist in firms today.  One mindset is the traditional one that believes that success is based mostly on building superior products and services and that customers will automatically follow. The other mindset is based on the realization that today clients are demanding experiences and outcomes more than ever before and that establishing real relationships with customers is today’s secret to success.

As it turns out, many companies are discovering that today the traditional approach of efficient execution and superior products is being increasingly commoditized.  And further, that the competitors of the future will not be the traditional ones.

In reality, a combination of both mindsets is important for success.  These mindsets are not mutually exclusive, and each can be measured in the following manner:

  • Firm IQ: This is the logical component of every organization. This includes all the people and capabilities that create products and new technologies. It’s also the part of the firm’s ethos that drives reliable and efficient execution.
  • Firm EQ: This is the emotionally intelligent ethos of every organization. This EQ includes the people and capability that allows the firm to empathize with its customers by delivering engaging experiences that culminate in real customer relationships.  It is also the social capability of the firm that is required to truly have genuine engagement with its customers, partners, employees and everyone else in the firm’s community.

So how does this relate to a typical firm and its success?  Let’s start with the reality of competition in today’s world.  Firm’s today live in intensely competitive environments and they constantly face downward pressure on prices and financial earnings. Most technology suppliers are commoditized to near zero margins.  And new technologies like Data, AI, and machine learning based applications are starting to disrupt virtually every type of business today from finance to pharmaceuticals, healthcare to telecommunications.  Amazon is now disrupting much more than just retailers. Literally, everyone is trying to figure out how they will compete in a world where whatever they make will probably be offered as a free or ultra-low-cost service by someone else.

And while all of this competition is going on, today’s firms look for relief by counting on differentiation, i.e. better or cheaper products, often supported by using the latest in new technologies.  All of this sounds completely logical, particularly since it is usually driven by the high IQ part of the company’s mindset.  The only problem is that what has worked in the past will not work in the future.

Firms that create differentiation today don’t do it with only with execution speed or technology advancement.  The bigger factor of product and service innovation today is customer engagement and their ability to build genuine customer relationships.  And to accomplish this, firms require more than IQ, they need an enterprise-wide EQ, or a seamless obsession with the customer’s experience across the entire organization, whether that experience is through people or aided by technology. It is what we call “Applied EQ.”

Name any of the more innovative, glamourized firms today (e.g. Amazon, Apple, Tesla, Netflix, etc.).  Yes, they can execute.  But what do they execute is the question?  They execute in a manner to develop scalable and genuine customer engagement and relationships.  For example, many firms sell phones and music listening devices, but Apple’s offerings emotionally connect, which means that people will stand in line all night to buy their next product — even if they don’t know why they need it.  They have a relationship with Apple products. Netflix is the firm famous for creating 20 million customer relationships, but without relying on a customer support center.  They did it with an algorithm and an engaging computer interface. Through this algorithm and the data they collect, Netflix knows better what you want, and they even know it before you know you want it (in many ways like Amazon).  It’s very hard for other firms to compete with that.  In the older Blockbuster Video model of the world, they tried to have customer relationship using live “people to people” conversations.  The result was, of course, ineffective, and eventually Blockbuster became irrelevant.

In fact, we have now come to the point in our technology capability where machines and computer interfaces can detect and amplify emotional engagement.  Your computer will soon know when you are losing interest in any topic and will be able to say to you, “why don’t we do this later when you are not distracted.”

Figure 1: Many firms could thrive if they execute well,
but EQ is the missing ingredient.

So, just like with people, success is a function of both the IQ and the EQ.  And while execution and technology are necessary (IQ types of factors), success is at least equally and sometimes more related to emotional intelligence and effective relationship (EQ types of factors).  This is true for firms, just as it is true for individuals.  And take note, even the firm’s technology should support the emotional intelligent and empathetic needs of the firm and its customers.  In fact, great technology-focused people can now truly add value to a firm in a way that could never be done before. The customer engagement and experience is now just as much a technical issue as it is a human-centric issue because of advances in software, AI, ML, and data.

This is where the Applied EQ comes in.  So many firms already have execution skills and often they have a great advantage in the business logic and/or technical capability, but they still struggle.  What they are missing is the EQ to maintain customer loyalty, understand the true needs, and set technology strategy that leads to meaning and purpose for the firm and its customers.

This is where so many CTO, CEO, and even McKinsey have gotten it wrong!  The recommended actions for a firm’s strategy generally come in 2 unhelpful categories:

  1. Completely Naive: Let’s catch up, become more innovative, and use/acquire advanced technologies like Data, AI, Blockchain, Machine Learning, etc.
  2. Logical, but ineffective: Let’s look at our business purpose first. What is the business strategy?  Then let’s see if and how technology can make it work better.

The second option does seem reasonable, but it is still missing the key elements of EQ which is the heart of the customer relationship.  With Applied EQ, we are in a global dialog with firm leaders to insert the missing ingredient back into the planning and learning process.  The path that we have seen to be most effective is the following:

The EQ Forward Path:

  1. The Reality Check: How would you make money if the cost of your current product or service was no cost or low cost?  Who do you think you are competing with?  Where could disruptions in your industry come from?
  2. The Response: What strategy will allow you to adapt to reality while considering the customer pain points?
  3. Your EQ Strategy: Do you know how best to engage with these target clients? Can you build genuine relationships? How? What emotional connection can our customers have with our products, services, and firms? What emotional need or larger purpose is related to our product, service, and customer relationships?
  4. Align the technology: Do you know how to leverage today’s emerging technologies to develop these engaging experiences and strategies? What role is Data playing in your firm? Do you know who else is collecting data on your customers? What is the innovation or technology can we invest in to scale our ability to emotionally connect
  5. Align the Human Capital: Who on your team has the best empathy for the customers. What is the best path forward from a low or moderate social/emotional engagement to a relationship that has a high level of social/emotional engagement?

Does your staff have the necessary level of EQ to take your firm to this next level with your customers? Today’s innovators at every level of the firm need a mindset which is obsessed with the needs of the customer and is truly empathetic with respect to engagement.

No longer is client satisfaction enough to scale your business, clients today are seeking out those companies that are obsessed with delivering client experiences that are seamless and engaging. You need to be obsessed with your client needs.

Our work in Big Data and Machine Learning has taken a new meaning, as what was once a “nice to have” is becoming a critical factor of what separates successful future firms from those that will be left behind. Even Startups and SMEs today need a Data Strategy that delivers on customer experiences.

The promise of Data has arrived. Is your firm ready for it?

The post The Applied EQ Project: EQ-Driven Technology Disruption appeared first on UC Berkeley Sutardja Center.

What is your data project missing? Here are three must-have elements

The other day I was having a discussion with Inder Sidhu, who was speaking in our Engineering Leadership Professional Program.  I wanted to understand his perspective on data and strategy. As you may know, he is a great person to talk to about these things.  He joined Cisco and helped it grow from $1 billion to $50 billion. He has literally been ‘written’ into the Cisco case and he is most famous for developing their brilliant channel strategy. He is also the author of a leading book on technology strategy titled Doing Both: Capturing Today’s Profit and Driving Tomorrow’s Growth.  

Our conversation was in the context of the work we have been doing in Data-X, a program which makes it possible to rapidly learn and implement amazing data and digital projects.  I was mainly interested in his thoughts on the issues that leaders face as they implement data projects.

His initial reaction was really quite insightful. From his perspective, there are three parts to how any company or organization creates value using data, AI, or blockchain (i.e. Data-X).  

  1. The first part is really about knowing “why” and understanding the relevance to the organization.  Typically, this part is also related to developing the entrepreneurial data-story and recruiting stakeholders to a project.  It may additionally include researching the use cases from other people or organizations. This is needed to evaluate what is possible and what the solution might look like within the current organization.
  2. The second part is the “what”, and it is related to understanding the capability of the actual technologies and algorithms that might be used.
  3. The last part is about the “how” and includes the ability to implement it.  This is everything from the actual tools to the behaviors and processes that should be used. This part also includes the holistic thinking needed to combine all these elements in a way that leads to an on-time, and working implementation.  Note that this is partially a learning process, not only an execution process.


An insightful point here is that when people study, teach, or discuss data, AI, and blockchain applications today, most of the focus is on the “what” in part 2.  It feels like everyone only concentrates on the algorithms and technical capabilities. So in reality, the first and last parts are grossly underserved.

This is an important point.  For years, we have been working on the “why” as well as the “how”.  In our terminology, the “why” for organizations has been developed within Berkeley’s Method of Innovation Leadership.  This perspective is the reason that leaders from Google, Apple, Samsung, and so many more global firms attend our innovation leadership programs. On the other hand, the “how” is what we have been developing in our Data-X program.  

Through this conversation, Inder has helped reinforce to me how these frameworks are synergistic and really work together.  My experience is that while executives benefit from innovation leadership (i.e. why), they also need to understand some of the “what” and some of the “how” in data frameworks; or else they really can’t lead digital transformation projects.  

On the other hand, I’ve also experienced that while engineering or technical leaders learn powerful tools and technical understanding for developing data projects (i.e. “how” and “what”), they still need to understand some of the “why” in the form of innovation leadership and business “relevance;”  else they simply cannot find common ground with the business executives, and eventually, their projects will implode.

This cross-pollination between implementation and leadership has proven to be critical for innovation.  We have noticed that when you learn one, it is important to learn at least a little bit of the other.  And with this focused insight, you can see our perspective on “making the magic happen” in your next innovation project.

The post What is your data project missing? Here are three must-have elements appeared first on UC Berkeley Sutardja Center.

Learning Rapid Implementation of AI and Data

What’s Golf Got to Do With It?

Our ability to deliver on the promises of AI, data, and other digital transformations is key to our national agenda and global security.  Just imagine what would happen if another, less friendly nation educates more people to readily develop these technologies. It’s pretty much the same as losing the industrial revolution and becoming irrelevant, both from an economic and national security point of view.

So how do we currently teach keen people to climb the steep learning curve needed to develop these technologies?  Today, we basically teach the theoretical foundations for each subject and then hope for the best.

If we taught people to play golf the same way that we teach people to master complex topics at universities, then it would be a miracle if we actually developed good golf players.   In our hypothetical golf curriculum, we could expect that in year one, students would only be allowed to place the ball on the ground, and they would practice this for about one year.  What about hitting the ball? No, that will happen much later. In the second year, we would focus completely on the backswing. How about hitting the ball in year three? No, the third year will be focused on understanding the materials inside the ball, and maybe calculate the optimal number of dimples for best flight.  Finally, after another year of golf theory, everyone graduates. Everyone is supposedly an expert at Golf. The only problem is that no one in the class has ever played it or even hit the ball. But of course, that can be learned on the job!

In Our Model, You Start By Hitting the Ball First

This is what we are trying to fix with AI, ML, and blockchain in our Rapid AI/Data-X courses.  A student who takes a different approach to learning these subjects may learn many theories without understanding how to actually implement an AI or data science solution.    

So here is an important idea for experiential learning; Would it not be better to start by letting students just hit the ball, and then find out what they need to improve?  Some coaching along the way would also have a very positive effect.

This is the method we decided to try for AI, ML, and other data implementations.  We would teach students how to use the fundamental tools at a simple level, and then see what they can do and what they need to learn to do next.  And while coaching is part of the process, a significant part of learning is self-driven with the goal of making a challenging project work by the end of a short period of time.

We tried this rapid type of implementation in our Data-X class at Berkeley.  And even to our surprise, we found that our students actually learned to create amazing projects in just a few weeks, not 2 years! We saw projects that identified knee problems from an MRI/CAT scan, that predicted energy use from past data, and that can identify fake news. With almost 150 students in the class, we are seeing over 25 projects in a semester.

Now don’t get me wrong, this model is not at all against theory.  It is absolutely essential to focus on the theoretical frameworks at every world-class university.  However, in this case, we are working to fill an implementation gap. Theory is still absolutely necessary, but it has to be the right theory, i.e. relevant and connected to the implementation and tools.  In fact, the course we developed includes a different balance of a) relevant theory, b) understanding of tools, and c) an open-ended project that serves as a defining challenge.

Our learning lesson: It seems to work!

Let me share an example of a comment I saw from a student who took the class:

“I think this class is so awesome because it teaches the tools and concepts that are most commonly used in workplace teams that are involved with data science and applied machine learning. The vast majority of teams that I’ve applied to within the past year use the tools taught in this class. When I arrived at my data science internship this summer, I already knew how to use most of my team’s stack.”

I cannot say that this is particularly good or bad as a first result, but it frankly seems promising to me.

How We Put It All Together:

For our rapid, implementation-oriented method to work, we needed to bring innovation behaviors into the approach.  In the first 4-5 weeks of the class, we actually do not ask students to write code for their projects. Instead, we have them develop what we would consider an “insightful story” or “narrative” that describes what they intend to build.  

In real life, every successful project, innovation, and/or venture starts with a story narrative.  Typically, a story is used to test the concepts as well as develop interest from other stakeholders of a project.  By week five, students convert their story into a “low tech demo” which captures many aspects of the project’s implementation, but without code. It is a super-light prototype which can be easily modified until it’s “right” in multiple ways.  

This approach is common to design as well as entrepreneurship, and it is also good practice for students to learn this innovation behavior.

After the low-tech demo, students get the green light to go into an agile development cycle and prepare for a demonstration in eight more weeks.  Similar to the MIT Media Lab, the slogan here is not “Publish or Perish,” instead, it is “Demo or Die.”

After the low-tech demo, students get the green light to go into an agile development cycle and prepare for a demonstration in eight more weeks.  Similar to the MIT Media Lab, the slogan here is not “Publish or Perish,” instead, it is “Demo or Die.”

There is a solid stream of math theory lectures, notebooks with code samples, and explanations of open source computer science tools along the way.  These components (project, tools, and theory) when integrated in the right balance, result in a very rapid experiential learning curve.

Key concepts: Story first, low tech demo, agile sprint, coaching for innovation behavior, and the three integrated elements of theory, project, and tools.

There is much more nuance than we have been experimenting with. I can say from experience, there are many ways for engineering projects to go wrong and we are correcting for many of these issues in different ways. I’m sharing our experience with this approach because even at this early stage, it does seem to be promising.  


The post Learning Rapid Implementation of AI and Data appeared first on UC Berkeley Sutardja Center.

Our Students Can Make Data, AI, & Blockchain Projects Work in Real Life – and So Can You

A Focus on Implementation and Rapid Impact

A Signal in the Noise 

About 2 years ago, I had a thought that it was time to offer a new type of course in the areas of AI/Data, and possibly extensible to the other digital transformations like Blockchain. To create anything of significant value in this area is no small challenge at UC Berkeley because it would have to be amidst the gigantic contributions of amazing people who have already done so much in data, AI, and computing.

After all, this is the institution that developed Berkeley UNIX, introduced open source to the world, created floating point, RAID Disk storage, relational databases, and many of the most famous machine learning algorithms and tools. And the scope has ranged from the most seminal theoretical (such as the NP-completeness of some of the world’s hardest problems) to the rollout of Spark which has become the defacto method of managing big data.

Of this wide spectrum of people and capabilities, I just happen among the set that likes to focus at the more applied edge of the spectrum. Over the past 12 years, I’ve spent a great deal of effort to bring a true experiential learning component to subjects which are often considered complex to understand.  I have worked on this because, as Berkeley, we want to balance the theory with practice.  Over the years, thousands of students have taken courses from me and my Center because of this applied perspective.  Leaders everywhere hire our students because they possess all three of these important characteristics:

1) an incredible technical depth
2) a holistic understanding of the larger problem
3) the psychological behaviors needed for real-life innovation.

The same is true even for my approach to a Data Science/AI type of course.  My goal was to make it the class that I would want to take. That means a class where you actually learn the current state of the art software tools and have the ability to create real-life applications.  This would be in contrast to solving artificial or toy problems. At Berkeley, we teach the class as “Applied Data Science for Venture Applications”, and informally, we have referred to this very applied, practical framework as Data-X.

An important part of the objective is literally to add more emphasis on the implementation.  To start, if you can’t actually create or use the technology, then it’s actually a major problem, whether you are a student, a company, or you are concerned about our national agenda.  Fast forward 2 years, we are currently running one of this course for the 3rd time, and we are seeing amazing results. In only 3 months, students with only python programming and some background in probability are able to create applications that predict energy prices, detect knee problems from MRI scans, crawl the web to create new data sources, and even identify fake news.

On the other hand, if you look at technology projects in most organizations across the world, you will quickly discover that they are often challenged to deliver working implementations.  In fact, many things can go wrong. Sometimes people can’t even the connect the theory with the practice.  At a deeper level, there are also issues when people understand the theory but do not understand the software toolsets.  It is like trying to build a skyscraper with sticks and mud instead of steel beams. Modern open source tools (e.g. tensor flow, pandas, etc.) have become incredibly powerful, but if you don’t know how to use them, you are reduced to improvising, instead of using pre-existing high-quality building blocks.

There are some Computer Science curriculums in the US and around the world which have become almost all theory and very little practice.  Berkeley has never had this narrow view, but in numerous other places, students rarely build things that matter until they are at their first job.  It’s a continual battle about the role of the university. Academics in some disciplines have historically considered hands-on experiential education the role of a trade school, whereas the opposite is true in the medical field.

In this area, we need both theory and practice because the correct elements of theory serve as a map to understanding the practice, and conversely, the practical, experiential view of the subject is necessary to genuinely understand the theory.  You just can’t have one without the other. But the balance and nuance must be right.

Going one level deeper, if you look at the teaching approach in many technical curriculums, most of the focus is on the theories needed to create the next generation of powerful tools, but less focused on theories required to effectively use the currently existing tools.  That part is left to the student. Again, we are not wrong for teaching fundamental theory, this discussion is only about the approach needed to fill the gap when it comes to the practice of implementation.

Besides the gap between theory and implementation, there are actually still many other things that can go wrong in real life projects, particularly when people and organizations are considered as part of the scope:

  • Sometimes people are organized in silos, which means the holistic solution can get lost between the experts.
  • Sometimes, the problem is from overdesign, i.e. too complex, too expensive, or an approach that just takes too long.
  • Alternatively, it may not even be technically possible.
  • And in yet another form of failure, teams without the right balance of skills simply don’t know what they are doing, and then they simply fall apart mid-project.

Specifically, in the domain of Data, AI, and other digital transformations, history has shown us from the last industrial revolution that those who created the new machines (or at least learn to use them) ended up doing well.  In contrast, those that resisted or simply didn’t adapt were no longer able to stay relevant.  But to build successful new technology capabilities requires a different type of technical learning.

And yet, the student teams that we teach don’t suffer from these same problems.  This is because they are actually learning both the technical and behavioral components necessary for innovation inside a framework developed for implementation.  And this is what we have been developing within our Data-X framework for rapid impact. And amazingly enough, it seems to work.

The point here is that we have an approach that helps students make Blockchain, AI, and Data projects work in real life. It’s not mysterious, and it’s actually quite tangible. So for all the firms and organizations who have been thinking about these type of projects – you can make it work as well.

The post Our Students Can Make Data, AI, & Blockchain Projects Work in Real Life – and So Can You appeared first on UC Berkeley Sutardja Center.