Dr. Michael Edelman talks quantum dots with Gigaom fifianahutapea.blogspot.com

Dr. Michael Edelman joined Nanoco in 2004, led the initial fund–raising and spun Nanoco out of the University of Manchester. Prior to Nanoco, Michael held a number of executive roles including responsibility for licensing the technology developed by GE/Bayer joint venture, Exatec LLP, Vice President and Managing Director at yet2.com , Commercial Director at Colloids Ltd and Business Manager at Brunner Mond & Co ltd., Michael started his career with ICI, has a Ph.D. in organo–metallic chemistry from the University of Sussex, UK, and undergraduate degree in classics and chemistry from Tufts University, Boston, MA, USA.

Dr. Michael Edelman will be speaking at Gigaom Change Leaders Summit in Austin, September 21-23rd. In anticipation of that, I caught up with him to ask a few questions.

Byron Reese: Tell me the first time you ever heard about Nanotechnology?

Dr. Michael Edelman: Oh gosh, probably in the late eighties. And in the late eighties, we weren’t really calling it nanotechnology then, we were calling it colloidal chemistry, which is chemistry on a very small scale and then the nano name took off. Nanotechnology has been around for thousands of years, starting off with some of the early pigments and dyes used by Greeks and Romans to paint pots. So it’s not a new concept, chemists, physicists have been working on these sorts of technologies for a very very long time, and typically what we mean by nanotechnology is materials, things under 75 to 100 nanometers. You are looking at working with sizes 10,000 times smaller than the width of a human hair. So pretty small.

Wow us a little bit with some of the science fictiony things we may live to see that nano is going to enable.

With Nanoco, my company, we play in the area of florescent semi-conductors called quantum dots. What’s unique about these materials and nanomaterials in general, is that they start to behave in weird and wonderful ways when they get very small.

And the amazing thing that our materials do is they fluoresce, they give off very, very bright, different colored light; red, green, blue, orange, yellow, whatever color you want. And that color is strictly dependent on the size of the nanocrystal. We are manufacturing these nanocrystals with a diameter between one and ten nanometers which is ten to one hundred atoms across.

We accurately manufacture these materials, growing these crystals of one, two, three, five, seven nanometers. It would be as if you had a very tiny golf ball with a diameter of one nanometer, and you expanded it. The chemical makeup is the same, but the mass is changing and this changes the electronic properties, which in turn changes the optical properties or color of light emitted.

When you have a material that lights up very brightly with only tiny amounts of energy, people start getting excited. We can bind specific anti-bodies to the quantum dots and use them to more accurately image and diagnose cancer. They absorb energy so they can also be used very effectively as new generations of solar cells.

So the ‘wow factor’ for the materials, these quantum dots, is that it’s a true platform technology that can be used across a number of different and unrelated end use applications from cancer imaging to next generation displays.

I get excited because it is very infrequent that you see a true platform technology. It is a word that is overused today. People talk about platform technologies all the time, but when you see a material that actually can be used in a number of unrelated sectors its tremendous.

Dr Nigel Pickett, our CTO and co-founder and I started Nanoco in the UK, in a converted men’s bathroom at the University of Manchester and have grown very successfully since then.

So you’ve actually expanded into the woman’s bathroom at this point?

[laughing] We’re actually much bigger. HP started in a garage and we started in the toilet.

Well you’ve got no place to go but up from there.

Well it was a big toilet.

In what sense are quantum dots quantum?

Because you get what we call a ‘sized quantization effect’ which is where the electronic properties of the semiconductor materials are changed, meaning the band gap of the material can be altered by changing the size. That is the quantum effect.

And how will they be used in quantum computing?

Our main focus today for these materials is on things that require enhanced color, so as a company we are not working on quantum computing. The folks working on quantum computing, using more traditional semiconductor technology, use molecular beam epitaxy to grow these quantum dots on wafers. That is the area that’s focused on the quantum computing.

We are essentially chemists and we are making these quantum dots in chemical reactors. In essence we are high-tech cooks, we add ingredients, we stir those ingredients and we heat them. How we do it is fairly sophisticated but the advantage of this is that the finished product is very cost effective to make, so we can apply these onto TVs today. Those TVs are at a price point that you and I can buy. Whereas quantum computing today is not there yet.

So where are you from a commercial standpoint with your technology?

The technology right now is getting launched into the marketplace. The Company has signed a number of deals, and probably [the one] that we’re known for is with Dow Chemical. Dow has built a large facility in South Korea to service the display industry, mainly the Korean TV giants. And the first products coming online are products from Samsung. You can go to Best Buy today and buy a new Samsung TV with enhanced color that comes from quantum dot technology. The first market to take off is the display market, and in the display market the first products are the high end color enhanced 4K displays. We are talking about LCD TV’s and LCD is the predominant display technology out there with about 240 million LCD TVs being sold each year. We’re helping the LCD technology, which has been around for a number of years, evolve and continue to get better.

Likewise, for lighting systems we have developed some products that we launched earlier in the year into horticultural lighting. What we’re doing is tuning the LED light with the quantum dots, so the light emits specific wavelengths that promote specific plant growth.

Looking forward in the next two or three years, what are some breakthroughs our readers should just keep an eye out for in the news?

The televisions are here now, they are getting rolled out and you are going to see a lot more of them. [Also] light and different types of light sources using quantum dots are here and you are going to see more quantum dot based lighting.

What I am excited about, if I look three to five years down the road, is the use of these materials in biological imaging and life science applications. Because our materials are all heavy metal free, [they can be used in the body] to very accurately image and diagnose cancer at an early stage, at a very sensitive level. You can tag specific anti-bodies onto these quantum dots of whatever color, and manipulate the size so they can get through the cell walls. Then they can bind specifically to a cancer that you are targeting. This, to me, is amazing.

Thank you so much for your time. I look forward to discussing this further in September.

Michael Edelman will be speaking on the subject of nanotechnology at Gigaom Change Leaders Summit in Austin, September 21-23rd.

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Teach Millennials How to Be Managed By You fifianahutapea.blogspot.com

This article is the fifth in a series of six. It is excerpted from Not Everyone Gets a Trophy: How to Manage the Millennials by Bruce Tulgan

Set Clear Ground Rules Up Front

Managers tell me every day that Millennials fail to meet a lot of unspoken expectations about behavior in the workplace. I have an idea: Speak them!

One credit union manager was telling me about a young employee who routinely came to work late and then made lots of personal calls on his cell phone throughout the workday. “Do I really need to tell him, ‘Come to work on time, and it’s not good to make so many personal calls all day long’?” Yes! You have to tell him, up front and every step of the way.

You have to figure out what your expectations are and then speak up. Set ground rules. Maybe there are corporate policies in place already. But often there are no concrete policies to regulate important intangibles like attitude, tone of voice, and other subtleties of professionalism in the workplace. You may need to figure out these ground rules on your own. You may need to say, “Whenever you are working with me, on any task, for any period of time, these are MY ground rules.” Then lay out your ground rules in no uncertain terms, and make it clear they are deal breakers for you: you can’t work with someone who doesn’t follow these ground rules.

Leverage the Power of High-Structure, One-on-One Meetings

Remember that Millennials have grown up hyper-scheduled. They thrive on that kind of structure, and they thrive on one-on-one attention. One of the most effective ways to help your young employees learn to be managed by you is to schedule regular discussions with each of them about their work.
At first, err on the side of meeting more often with each person—every day, every other day, or once a week. Start by evaluating what time will best work for you: What time will fit your regular schedule and needs? Then communicate with each Millennial the expectation that you will meet regularly one-on-one at a regular time.

Making a plan with your young employee to meet one-on-one at a regular time and place is a huge commitment for both of you. It is a powerful statement that you care enough to spend time setting this person up for success. When you follow through and spend that time, you are creating a constant feedback loop for ongoing short-term goal setting, performance evaluation, coaching, troubleshooting, and regular course correction.

Spell out how long you expect each meeting to last (my advice is to keep them to fifteen or twenty minutes). Don’t ever let these meetings become long or convoluted. Make it clear that your meetings will follow a fast and tidy agenda, preferably the same basic format every time. Start each meeting by reviewing the agenda. Whenever possible, present an agenda in writing that you can both follow. These meetings should be cordial but all business. This is not the time for chitchat.

Like everything else, this dynamic process will change over time, and your approach will have to change with each young employee you meet with regularly. For each of your employees, you’ll have to figure out how often to meet, how much time to spend at each meeting, what format to use, and what topics to cover. And remember: you’ll have to make adjustments over time.

No matter how well things seem to be going, you still need to verify that things are indeed going as well as you think. If they are, make sure that Millennial knows just how many points she is scoring today.

Create an Upward Spiral of Continuous Improvement

Managers often tell me they have a hard time talking to Millennials about failures great and small. “When they make a mistake, you hesitate to tell them because they take it so hard,” I was told by a partner at a prestigious law firm. “They seem to take it personally, like you are breaking their heart. I want to say, ‘Don’t feel bad. Just go back and make these changes, and then next time try to remember to do it properly in the first place.’ That seems pretty basic.” It is pretty basic.

When it comes to addressing Millennials’ performance problems, the most common mistake managers make is soft-pedaling honest feedback or withholding it altogether. Sometimes managers take back incomplete work and finish it themselves or reassign it. Other times the problems are not addressed at all, and the work product remains substandard. Millennials are left to fail unwittingly or improve on their own impulse and initiative. As one Millennial put it, “What do you want me to do, scream it? Beg for it? Help! Help me get it right. Help me do it faster. Help me do it better. Help me improve.”

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About the Author

Bruce Tulgan is an adviser to business leaders all over the world and a sought-after keynote speaker and seminar leader. He is the founder and CEO of RainmakerThinking, Inc., a management research and training firm, as well as RainmakerThinking.Training, an online training company. Bruce is the best-selling author of numerous books including Not Everyone Gets a Trophy (Revised & Updated, 2016), Bridging the Soft Skills Gap (2015), The 27 Challenges Managers Face (2014), and It’s Okay to be the Boss (2007). He has written for the New York Times, the Harvard Business Review, HR Magazine, Training Magazine, and the Huffington Post. Bruce can be reached by e-mail at brucet@rainmakerthinking.com; you can follow him on Twitter @BruceTulgan, or visit his website.

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Agile DevOps: A Path to the Common Ground of Productivity fifianahutapea.blogspot.com

 

Agility has become the buzz word around the enterprise. whether it is agility around storage, networking, cloud operations, or most any other IT service is not really the point here, it all comes down to agility as an ideology.

Take for example the burgeoning data analytics market, which is driven by big data and business intelligence, where implementing agile ideologies could be the secret to success. After all, an agile business needs to be able to react to trends and discoveries to remain competitive, and waiting on analytics does not bode well for those looking to make intelligent decisions as quickly as possible.

In other words, best of breed analytics solutions must bridge the gap between data science and production to unify development and deployment into an agile methodology. With that in mind, Florian Douetteau, CEO of Dataiku, has put together an interesting guidebook that discusses how to achieve that level of synergy to build a data project that embodies the ideologies of agility.

Douetteau has identified the key strategies that illustrate how to bring agility to a data science project, those strategies include adopting:

–              Consistent Packaging and Release

–              Continuous Retraining of Models

–              Multivariate Optimization

–              Functional Monitoring

–              Roll-Back Strategy

–              IT Environment Consistency

–              Failover Strategies

–              Auditability and Version Control

–              Performance and Scalability

Ultimately, the goal here is to bring agility to the data team, where a data science team and IT production can work hand in hand to deliver results in an agile fashion.

In an Interview with GigaOM, Douetteau offered additional advice, he said “One of the most valuable tips I can offer is that IT should provide a common platform, which gives users across the different groups access to the tools and technologies they are familiar with. Ideally, visual drag and drop tools for should be provided for less technical team members, while the ability to code, should be provided for advanced members. What’s more, monitoring, security options and role based administration tools should be made available to those responsible of deployments.”

Nonetheless, previous attempts to achieve the goal of agile decision making has been an almost impossible task, thanks to the silos surrounding data science development and the deployment of operational applications that can illustrate results.

Douetteau says “the biggest challenge of most data science projects is getting everyone on the same page in terms of business goals, technical requirements, project challenges, and responsibilities. More often than not, there is a disconnect between the worlds of development and production. Some teams may choose to re-code everything in an entirely different language while others may make changes to core elements, such as testing procedures, backup plans, and programming languages.”

It is that isolationism that prevents many data science projects from becoming an overall success, and worse yet, lead to incorrect conclusions and assumptions. Much of the blame can be placed upon the waterfall development ideologies of the past, which have hampered the adoption of agility in the area of data sciences.

Douetteau adds “preventing failures takes a manager who is willing to act as tech stack and programming language dictator, who will force the team into a fixed technology for a solution. That manager should also ensure that team members adopt a big picture approach, where they are able to help each other complete tasks outside of their comfort zone. Individual silos of knowledge will hinder a team’s effectiveness, and collaboration is the key to success.”

For enterprises to truly become agile, they must eschew those waterfall development processes and switch to agile methods across the board. However, data science projects seem to be the most opportune place to start in today’s on demand, instant results world.

Douetteau adds “Providing a platform that caters to all members of the team promotes collaboration and communication, two elements that are essential to the success of any devops/data analysis project that involve multiple departments.”

What’s more, the lessons learned on data science projects can be readily applied to other areas of IT and business operations, making agile an achievable goal, as long as you know where to start.

Douetteau says “Finding a common ground between your data team and IT department will undoubtedly ease the process of creating a data product for your organization.  If all of your teams are aligned from the start of a project, each department knows their role and what technologies they are familiar with and specialize in to accomplish the task. Data scientists can build a solution and the IT department can deploy it.   Once a best practices procedure is established it can be reproduced and your organization can more quickly and effectively make use of new predictive data opportunities… making your organization truly agile”

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Manoj Saxena talks Artificial Intelligence with Gigaom fifianahutapea.blogspot.com

Manoj Saxena is the executive chairman of CognitiveScale and a founding managing director of The Entrepreneurs’ Fund IV, a $100m seed fund focused exclusively on the cognitive computing space. Saxena is also a Special Advisor to IBM senior leadership where he focuses on operationalizing IBM’s $100m Watson Cloud Ecosystem Fund and making side-by-side investments with the TEF IV fund.

Saxena is also the chairman of two other startups in the cognitive computing space, WayBlazer and SparkCognition.

Prior to joining TEF, Saxena was general manager, IBM Watson, where his team built the world’s first cognitive systems in healthcare, financial services, and retail. He received the IBM Chairman’s award for Watson commercialization and helped with the formation of Watson Business Group in January 2014 with a $1B investment from IBM.

Saxena will be speaking on the subject of artificial intelligence at Gigaom Change Leaders Summit in Austin, September 21-23rd. In anticipation of that, I caught up with him to ask a few questions about AI and it’s potential impact on the business world.

Byron Reese: How intelligent do you think a computer can become?

Manoj Saxena: I think they can become super intelligent. They already are. In certain areas, they have far exceeded human brain capacity. Now they are super intelligent, they are not super conscious. So I separate intelligence from awareness and consciousness. So I think intelligence is here, has been here for decades. And you know with the advent of cheaper computing power in the cloud and more access to cloud through mobile, I think that intelligence is going to get more and more pervasive and will basically be woven into all aspects of our life. You know, how we work, how we live and how we play is all going to be changed through computer intelligence surrounding us. I actually talked about this notion of as a species Homo Sapiens are dead. Homo Digitus is the future because we will be surrounded by intelligence and amplified and augmented by intelligence.

Do you believe that in AGI, general intelligence is possible to build?

I think it is possible but we are probably at least 40 or 50 years away from it. You know artificial general intelligence which essentially you could argue that you know parts of it. Google, you could argue as the beginning of an AGI kind of like a mega brain or Watson in certain domain is the beginning of an AGI but through AGI covering all forms of human knowledge and human pursuits, I’ve read a study on it that even if you ask the specialist in the AI field, the average response was that we are looking at 2050 or 2060 by the time we will attain AGI. I think the most exciting part is not AGI but ASI, Artificial Specific Intelligence.

How so?

Well I think a little bit of AI can go a long way. There were few big revelations when I was running IBM Watson. First, you don’t need to build an AGI to drive humanity forward or to transform businesses. A little bit of AI when applied to targeted consumer engagement or industry specific business processes can have exponentially huge impact. The second insight I had when I was running Watson is, the real interesting part about AI and machine intelligence is not asking the question of a machine, but it’s the machine telling you what question to ask. You know there is three types of information in this world: there is stuff you know, there is stuff you know you don’t know, and there is stuff you don’t know that you don’t know. The real interesting part of machine intelligence is the third bucket where the machine taps you on the shoulder and says, hey you got to check this out.

I don’t want to get bogged down in definitions or anything, but can you please explain the distinction between machine intelligence, artificial intelligence and cognitive computing?

Yeah. So artificial intelligence is sort of the uber category. Artificial intelligence is like saying ‘software’. It’s the broadest definition which includes multiple types of technologies and techniques: machine learning is one, computer vision is another one, and cognitive computing is yet another one. There are many other types of AI. So at a top level, AI is the super category and then within that, machine intelligence is application of AI where machines start learning and start getting smarter on their own. So it could be a thermostat, it could be a traffic light or it could be a mobile app. Any of these can get smarter. Cognitive computing is that specific part of AI that relates to mimicking the human brain in terms of how we understand, reason, decide, and learn as a human being.

And recently, you know Stephen Hawking has mentioned that AGI may be an existential threat. Elon Musk says things like, maybe we are just a boot loader for the machine intelligence and that’s the next step in evolution. Bill Gates is concerned about what a general intelligence could do. I would ask two questions. One, why do you think that so many obviously very smart people are worried about it and second, do you share that worry?

I think there is some truth to that worry that I share. But I also think there are other scenarios that are in my opinion overhyped and overinflated in terms of machines as the new digital overlords.

[CALL IS LOST. AFTER RECONNECTING:]

My car’s system is kind of acting up here. This is a good example of why I am not too worried about machines being our own overlords because you can’t even get the damn phone to work in your car or your autocorrect to work on your cell phone as someone said. Having said that, today we already are at a point where machines are running our lives. There are millions of us that entrust our lives to computers today by allowing a computer to land our plane and seem very comfortable doing so. And that will slowly expand that we could only get more prevalent as we start giving more and more trust to machines. Robotic surgeries of eyes or blood vessels are other good examples.

On the other hand, there is a lot that we don’t know about how the human brain works and it will be hard to replicate that in a machine. There is much to be learned around our own consciousness, compassion and instincts work for example so in that sense we are very far away from the worry of a new digital race of computers.

What is needed for sure are some general principles and governance by which we as a race put this powerful technology to work for the betterment of society. I am currently engaged in some discussions with industry and local leaders around AI ethics and moral responsibilities to prevent both real and perceived threats from an AI apocalypse.

So what are you trying to do with CognitiveScale?

What we focus CognitiveScale on is deep practical applications of machine learning in industry. So what CognitiveScale builds is the notion of industry digital brains. They have taken AI and applied it into three verticals in commerce, in healthcare and in wealth management. We call it health, wealth and commerce. We are using AI to transform how patients manage chronic conditions and chronic diseases. We are using AI to manage how shoppers are experiencing their journey with the retailer and how financial advisors and investment advice is being delivered to end users. So we focus on transforming the experience of a user through a mobile phone or a browser that creates an experience like that of the traffic and map application Waze.

Waze is a good example of an existing cognitive app. You know it’s an app that is able to source a lot of data both structured data and unstructured data and it’s an app that guides you through the journey and optimizes your experience and outcomes. It knows you, it knows what’s around you and it gets you to your destination in the most efficient fashion. So what CognitiveScale is doing is they are building products for health, wealth and commerce that create a Waze-like experience that lets a patient manage their diabetes or their cancer or their obesity by guiding them through their journey. It lets a shopper manage the journey of shopping for an event and it helps a financial client manage the journey of investment advice because we believe that patient shoppers and financial clients, they all go through a journey and these applications take a regular mobile app and they put a little digital brain behind it and those applications start acting like Waze.

And where are you in your product lifecycle?

CognitiveScale has launched two products. One is called Engage for the customers, the other is called Amplify for business processes. So Engage transforms how a customer experiences the company and Amplify makes every employee your smartest employee. They are about a 100 people and they have been in existence for about 3 years. They have 20 customers and global brands you know everything from Barclays to Nestle to Macy’s to Dow, Eli Lilly, MD Anderson. So they’ve got a tremendous technology and client proof points and have a very strong deal pipeline. Off to a good start but much more needs to be done.

Do you believe that computers will become conscious?

Well yes and no. So, yes but it depends on how you define consciousness. So with consciousness, there are two problems. One is, there is no consciousness detector today. So we don’t really have a model that says, okay what’s the level of consciousness in a particular human being. Now there are some models that are based on anatomy like doctors use to know if you are comatose or not or behavioral people use to figure out whether you are mentally capable or not but there is no proper sort of a continuous consciousness detector that we can use to measure a person’s or a computer’s consciousness or lack of it. So one is a problem of measurement.

And second is a problem of applicability because only, I think, 5% of the human brain is used around consciousness or consciousness-related activities and it may very well be that consciousness may be outmoded and may be outdated by computers that get super intelligent and are able to do tasks much more efficiently and maybe the relevance of what consciousness is needed for is a lot more limiting.

For example, how does it matter for a computer or when does it matter if the computer can sense the pain of a young wife who lost her husband, or if the computer can sense the joy and laughter of a child on a beach, or why someone would throw themselves in front of a running train to save a baby, right? So these are the kinds of things while they are important, they may not be as relevant in the grander scheme of things for the progress of humanity with machine intelligence. So those are the two issues. Therefore, in part yes, you could say the computers will get self-aware but I think unless we have a proper consciousness detector, it will be very hard to formally answer the questions if computers can become conscious.

What is your take on the Chinese room problem, which argues that computers can’t really ever be truly intelligent? [Note, this is a classic argument against the possibility of a general AI put forth by the philosopher John Searle. It is worth looking up in Wikipedia. But the basic idea is that because a computer is completely mechanistic, it simply follows programming. No matter how clever it looks, it doesn’t really understand anything.]

I think there is a lot of truth to that statement as long as you assume that computers are being built on the Von Neumann architecture. Under the present architecture the Chinese room problem that you are talking about is true. You can say the computer is only parsing things together. However, if you look at evolution of quantum computers, [it might be different.] When you ask a current computer what is 1 plus 1, it will get you 2. When you ask a quantum computer what is 1 plus 1, it will take all numbers on the right and all numbers on the left and it will give you all kinds of answers. So it won’t just add up 1 plus 1, it will add up 1 plus 5, five million plus 3 and on both sides and then it picks a particular quantum event. So a lot of theories [suggest] that the human mind is a quantum machine, and that the reason we make connections across things which may not have any logic to it. There is a big stream of expertise and thinking that believes that the human mind operates not like a traditional computer but more like a quantum computer. So if you took that approach then I think the answer could be, ‘yes AGI is possible.’

Great. We’ll leave it there. Thank you for taking the time to talk today.

Manoj Saxena will be speaking on the subject of artificial intelligence at Gigaom Change Leaders Summit in Austin, September 21-23rd.

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Does Blockchain hold the key to the distributed patient data dilemma? fifianahutapea.blogspot.com

By now most readers have probably heard of blockchain through tech blogs and major cover stories from the likes of The Economist over the past year. The financial sector has rapidly accelerated engagement with blockchain through a growing number of consortia and fintech startup initiatives. As the foundation for bitcoin, blockchain’s distributed, cryptographic ledger provides a novel data structure and capabilities that could offer a wide number of benefits beyond existing technologies over the coming decade.

The discourse on blockchain is exploding, as are the critiques. But many of us can’t help but feel that blockchain, in an ever evolving manner, is here to stay and is likely going to become the next layer of the internet that will dramatically improve security of data that is flowing in our transactional economy. Quite simply, we need blockchain’s cryptographic security and distributed data structure to deal with the wealth of data that is coming from the citizen-end of the spectrum.

Not least in the healthcare sector, where patient data is spread across an increasingly fragmented set of repositories. Healthcare’s interoperability challenge may only grow worse for the medium term as the growth of data from beyond the electronic health records (EHRs) due to wearables, smartphone apps and sensors in the home become more mainstream.

We see a number of bottlenecks arising out of this inability to integrate non-EHR data into records and become actionable intelligence for clinicians. This partially accounts for the lack of stickiness of most wearables as the data collected is locked in apps and fails to provide actionable feedback to those whom need it most.

A great deal of health data is locked in silos and under-utilized in both the diagnostic process and more broadly in medical research. Blockchain is one of several solutions that are only going to grow in importance, due to its distributed and traceable nature.

Meanwhile, healthcare is reaching an epic number of data security breaches over the past year including entire hospitals taken hostage by ransomware. With blockchain we may get a twofer by giving patients more control over whom they can share data with in clinical research, for example, while also maintaining higher levels of security.

Blockchain’s smart contract capabilities might also enable sharing economies for medical technology such as MRIs, expensive machinery that sometimes goes idle and could take advantage of the IoT and blockchain and enable new business models around scheduling and local options for consumers.

Blockchain has also recently been used to help fund novel HIV research. UBS, the bank, donated code to Finclusion Systems for a platform that will launch HealBond, a “smart bond” amounting to $10B deployed in a more efficient manner to fund research for HIV cures.

As healthcare slowly enters the API economy beyond siloed EHRs we may eventually see the post-EHR based on distributed databases and more patient-centric controls. Blockchain will likely play a major supporting role in this gradual transition that values data liquidity vs. data capture, patient-centric vs. vendor-centric solutions that we find in our current health IT ecosystem.

This will be good news for consumers and those interested in wellness, but this won’t happen overnight. We may also need to approach blockchain with the openness that typically hasn’t greeted “the new” in technology in the past. Play and experimentation will be needed to change entrenched ways.

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Will the robots take all the jobs? fifianahutapea.blogspot.com

This article is part of a continuing series leading up to Gigaom Change, which will be held in September in Austin, Texas.

Humans have always had a love/hate relationship with labor saving devices. Generally speaking, the owner of the device loves it and the person put out of work by it hates it. This tension periodically takes the form of violent rejection of industrial technology in all of its forms.

The cotton gin “did the work of twenty men” which meant that after it was installed, one fella loved it, but the nineteen newly-unemployed workers probably shook their fists at the infernal gin, wishing all manner of evil to befall that Eli Whitney troublemaker.

While this “technological unemployment” has been cited as the cause of our economic woes for two centuries, the issue has taken on a new since of urgency as their has emerged a general fear that the wave of technical innovation we are currently in will capsize the economy and produce a new category of workers: The permanently unemployed.

Is this another example of the “boy who cried ‘no jobs’?” or are we witnessing a true transformation in our economic world?

The question is fundamentally unknowable because it hinges on three independent factors, each of which is also unknowable.

The three factors are:

1) How many jobs will the robots/AI really take?
2) How quickly will that happen?
3) What new jobs will be created along the way?

Let’s dive in:

The tipping point of widespread permanent unemployment is thought by many to be the driverless car taking all the jobs away from the truck drivers:

Self-Driving Trucks Are Going to Hit Us Like a Human-Driven Truck

One Oxford study claims that 47% of US jobs could vanish in 20 years. While consulting giant McKinsey & Company says 45% of all work activities could be automated right now.

But at the same time, there is a chorus of voices urging calm and pointing out that in spite of radical transformations of virtually every industry, the US has maintained near-full employment. How can this be?

Technology has created more jobs than it has destroyed, says 140 years of data

Two interesting questions that need to be addressed when approaching these issues are:

1) Why are there still good-paying jobs in the West? Why hasn’t mechanization put everyone out of work?

Bring on the robots, please!

2) A century ago, Keynes predicted that in the future, due to labor-saving devices, we will only work 15 hours. Why hasn’t this in fact happened?

How to relax and start loving the robots

The widespread fear of substantial, permanent joblessness has caused the topic of a universal basic income to move to the mainstream. How would this work?

We talked to five experts about what it would take to actually institute Universal Basic Income

Finally, it may simply be that in a post-scarcity world, “working for a living” just doesn’t have the moral imperative that it used to. We might regard what Buckminster Fuller had to say on the topic:

“We should do away with the absolutely specious notion that everybody has to earn a living. It is a fact today that one in ten thousand of us can make a technological breakthrough capable of supporting all the rest. The youth of today are absolutely right in recognizing this nonsense of earning a living. We keep inventing jobs because of this false idea that everybody has to be employed at some kind of drudgery because, according to Malthusian Darwinian theory he must justify his right to exist. So we have inspectors of inspectors and people making instruments for inspectors to inspect inspectors. The true business of people should be to go back to school and think about whatever it was they were thinking about before somebody came along and told them they had to earn a living.”

Robotics, and its impact on business, will be one of seven topic areas covered at the Gigaom Change Leader’s Summit in September in Austin. Join us.

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US Judge confuses privacy and security, concludes that you should have neither fifianahutapea.blogspot.com

Senior U.S. District Judge Henry Coke Morgan Jr. a federal judge for the Eastern District of Virginia has ruled that the user of any computer which connects to the Internet should not have an expectation of privacy because computer security is ineffectual at stopping hackers.

The ruling made on June 23^rd was reached in one of the many cases resulting from the FBI’s infiltration of PlayPen, a hidden child exploitation site on the Tor network. After taking control of the site, the FBI kept it up and running, using it to plant malware on visitors’ computers, gathering identifying information that was used to enable prosecution.

JCM ruled that the FBI’s actions in hacking visitors’ computers did not violate Fourth Amendment protections and did not require a warrant, stating that the “Defendant here should have been aware that by going [on-line] to access Playpen, he diminished his expectation of privacy.”

JCM offered as an analogy a previous case (Minnesota v. Carter 525 U.S. 83 – 1998) which ruled that a police officer looking through broken window blinds does not violate anyone’s Fourth Amendment rights, so hacking a computer does not either.

“Just as the area into which the officer in Carter peered - an apartment - usually is afforded Fourth Amendment protection, a computer afforded Fourth Amendment protection in other circumstances is not protected from Government actors who take advantage of an easily broken system to peer into a user's computer. People who traverse the Internet ordinarily understand the risk associated with doing so.”

JCM notes that in 2007 the Ninth Circuit found that connecting to a network did not eliminate the reasonable expectation of privacy in one’s computer, but takes the position that in the last nine years things have changed enough to render this position outdated.

“Now, it seems unreasonable to think that a computer connected to the Web is immune from invasion. Indeed, the opposite holds true: in today's digital world, it appears to be a virtual certainty that computers accessing the Internet can - and eventually will - be hacked.”

As justification for this opinion, JCM cites the Ashley Madison hack and a Pew Research Center study on privacy and information sharing as evidence of the acceptance that hacking is inevitable. The Pew study looked at American’s attitudes to sharing personal information in return for receiving something of perceived value. Although the focus of the Pew report was on privacy and not security it did report that focus group participants “worried about hackers”. However, these concerns were expressed exclusively in terms of a hacker’s ability to gain access to personal data from compromised business computer systems, not personal systems in the home.

Judge Coke Morgan’s level of technical understanding appears to be highly selective. The same judge who ruled on a patent case between Vir2us, INC. and INVINCEA, INC. over competing claims covering advanced anti-malware products, fails to acknowledge that anti-malware products continue to advance. In offering that “Terrorists no longer can rely on Apple to protect their electronically stored private data, as it has been publicly reported that the Government can find alternative ways to unlock Apple users’ iPhones.” He ignores the level of expertise needed to identify the exploit that was used to access the phone used by one of the San Bernardino attackers, or that the hack in question was only applicable to the now superseded iPhone 5C. While it may be possible to unlock older iPhones running back-level OS releases lacking the most up-to-date security features, Apple continues to develop new hardware-based security features and works to fix security vulnerabilities as it finds them. While it is reasonable to claim that many computers are vulnerable to attack, in suggesting that this means it is “a virtual certainty that [all] computers accessing the Internet can – and eventually will – be hacked” or that there is nothing that can be done to mitigate this risk, JCM is either being deliberately disingenuous or is failing in his analysis.

Describing the ruling as “dangerously flawed” EFF Senior Staff Attorney Mark Rumold wrote “The implications for the decision, if upheld, are staggering: law enforcement would be free to remotely search and seize information from your computer, without a warrant, without probable cause, or without any suspicion at all.” But holds out that the ruling is “incorrect as a matter of law, and we expect there is little chance it would hold up on appeal.”

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