Guide to The Blockchain

Depending on who you ask, blockchains are either the most important technological innovation since the internet or a solution looking for a problem.

The original blockchain is the decentralized ledger behind the digital currency bitcoin. The ledger consists of linked batches of transactions known as blocks (hence the term blockchain), and an identical copy is stored on each of the roughly 60,000 computers that make up the bitcoin network. Each change to the ledger is cryptographically signed to prove that the person transferring virtual coins is the actual owner of those coins. But no one can spend their coins twice, because once a transaction is recorded in the ledger, every node in the network will know about it.

The idea is to both keep track of how each unit of the virtual currency is spent and prevent unauthorized changes to the ledger. The upshot: No bitcoin user has to trust anyone else, because no one can cheat the system.

Other digital currencies have imitated this basic idea, often trying to solve perceived problems with bitcoin by building new cryptocurrencies on new blockchains. But advocates have seized on the idea of a decentralized, cryptographically secure database for uses beyond currency. Its biggest boosters believe blockchains can not only replace central banks but usher in a new era of online services that would be impossible to censor. These new-age apps, advocates say, would be more answerable to users and outside the control of internet giants like Google and Facebook.

Unless, of course, Facebook runs away with the idea itself. In June, Facebook announced Libra, a new blockchain that will support a digital currency. Unlike the thousands of anybodys who run Bitcoin nodes, it will be controlled by an association comprised of just 100 companies and NGOs. Libra is certainly a challenge to central banks, not least because it’s a privately controlled monetary system that will span the globe. But replacing government with corporations is not exactly the revolution that enthusiasts imagined blockchain would bring. So far, the crypto community is divided on whether Libra is a good thing. Some see Facebook’s effort as a corruption of a technology designed to ensure that you don’t need to trust your fellow users—or any central authority. Others are celebrating it as the moment that blockchain goes mainstream.

Other so-called “private” blockchains, like Libra, are growing in popularity. Big financial services companies, including JP Morgan and the Depository Trust & Clearing Corporation, are experimenting with blockchains and blockchain-like technologies to improve the efficiency of trading stocks and other assets. Traders buy and sell stocks rapidly using current technology, of course, but the behind-the-scenes process of transferring ownership of those assets can take days. Some technologists believe blockchains could help with that.

Blockchains also have potential applications in the seemingly boring world of corporate compliance. After all, storing records in an immutable ledger is a pretty good way to assure auditors that those records haven't been tampered with. This might be good for more than just catching embezzlers or tax cheats. Walmart, for example, is using an IBM-developed blockchain to track its supply chain, which could help it trace the source of food contaminants. Many other experiments have emerged: Voting on the blockchain. Land records. Used cars. Real estate. Streaming content. Hence the phrase “xxx on the blockchain” as a catch-all for the enduring hype cycle. The question is, if one organization (say, Walmart) has control of the data, did it really need blockchain at all?

It's too early to say which experiments will stick. But the idea of creating tamper-proof databases has captured the attention of everyone from anarchist techies to staid bankers.

Get Paranoid About Securing Your Data

If you are not paranoid about cyber security, identify theft, or fraud, you should be.

Today’s hot news from the Washington Post:

“Marriott discloses massive data breach affecting up to 500 million guests.  The hotel giant said an unauthorized party had accessed the reservations database for Starwood hotels, one of Marriott’s subsidiaries. The breach included names, email addresses, passport numbers and payment information.” (Nov. 30, 2018)

If that doesn’t motivate you to protect your data, nothing will.

The financial loss can be devastating, But, it’s worse than that. It’s not just your financial information at risk. You expose your entire life, reputation, friends, address, travel plans, health information and other private personal information to grievous harm.

The Internet opens up an entire new universe. However, it can also be a bad neighborhood. But, in any bad neighborhood a little caution goes a long way toward cutting down your chances of being mugged.

To paraphrase an old Navy saying, a data breach can ruin your whole day.. Even if you recover every cent, it will lead to months of aggravation.The threat is real, persistent, and menacing. You need to set your defenses.

It’s critical that you secure your data, but much easier than you think to do it.  It’s not hard. Existing tools make it remarkably easy. Now would be a great time to get started.

Password management: The first step to building a moat around your data

It doesn’t take long to use a hundred different sites that require your log in. That’s for your security. But, nobody is going to remember 100 passwords. So, most people cheat, opening themselves up to unlimited mischief.

Right now get a good password manager that will sync across all your devices, and suggest really strong unique passwords like “or!MXY3$VLWw7eHD” for every one of your accounts. Of course, you are never going to remember this password but the application will, and it will open your sites directly from the password manager. So, you can have a different virtually unbreakable password for every site you log into. And, you will have secure access to them from any place in the world.

Password managers are available for all the major operating systems, easy to install, secure, synch data across all your devices and cheap. There just isn’t any excuse for not using them.

Browsers as password managers

Modern browsers like Google’s Chrome or Microsoft’s Edge will remember your passwords and sign you into any of your accounts once you are logged into either your Google or Microsoft account from any device in the world. Just remember to sign out any time you are not on one of  your own devices.

Simple passwords won't hack it

Even very sophisticated professionals get lazy and use the same password for multiple sites and/or use something simple like 123456, or abcdefg. They might as well wear a target on their backs.

A remarkable number of people use simple passwords. Searching Google yields the most used passwords:

  • 123456
  • Password
  • 12345678
  • qwerty
  • 12345
  • 123456789
  • letmein
  • 1234567

Really? Any child can figure those our in a few seconds. But most hackers are sophisticated, highly motivated criminals. It won’t take them too much longer to try variations of your birthday, address or spouse’s name.

Let’s be honest, any self respecting hacker has programs that will grind through a million possibilities in a few seconds. So, long complex passwords are essential to keep them at bay. Sixteen digits with upper and lower case letters, numbers, and special characters are a reasonable standard.

Plan for the worst

Get used to the idea that sites you use will be breached. But when it happens you must contain the damage. Don’t reuse passwords.

If you use the same password on multiple sites, one data breach opens up your whole life on all your other sites like so many falling dominoes. So, if Marriott gets breached (it did) it could open up your password for Amazon and other sites that use the same password, no matter how strong it is. Not good.

You know better than to write down your passwords. But, half of you probably have them neatly typed under your keyboard or in your top drawer.

By using your browser’s password memory, and a good password manager gets you down to just two passwords you have to remember. Of course those two passwords are the keys to your kingdom, so use a little thought on them. It gets even simpler and more secure if your devices have fingerprint or facial recognition. But you have to make it exponentially harder for a bad actor to access your data.

Start now

It’s going to take some time to change all your passwords. However, it will be well worth the effort. Just do a few a day, but get it done. Start with financial and credit card accounts. Then work your way down to the least important.

Once you are done your life will be a lot simpler, and secure.

There’s lots more to do before your moat is finished. But, if you don’t do these first steps, there isn’t much hope for you. Now would be a great time to get started.

The Internet is a jungle. Be safe out there.

Artificial Intelligence: What's The Difference Between Deep Learning And Reinforcement Learning?

The various cutting-edge technologies that are under the umbrella of artificial intelligence are getting a lot of attention lately. As the amount of data we generate continues to grow to mind-boggling levels, our AI maturity and the potential problems AI can help solve grows right along with it. This data and the amazing computing power that’s now available for a reasonable cost is what fuels the tremendous growth in AI technologies and makes deep learning and reinforcement learning possible. With the rapid changes in the AI industry, it can be challenging to keep up with the latest cutting-edge technologies. In this post, I want to provide easy-to-understand definitions of deep learning and reinforcement learning so that you can understand the difference.

Both deep learning and reinforcement learning are machine learning functions, which in turn are part of a wider set of artificial intelligence tools.  What makes deep learning and reinforcement learning functions interesting is they enable a computer to develop rules on its own to solve problems. This ability to learn is nothing new for computers – but until recently we didn’t have the data or computing power to make it an everyday tool.

What is deep learning?

Deep learning is essentially an autonomous, self-teaching system in which you use existing data to train algorithms to find patterns and then use that to make predictions about new data. For example, you might train a deep learning algorithm to recognize cats on a photograph. You would do that by feeding it millions of images that either contains cats or not. The program will then establish patterns by classifying and clustering the image data (e.g. edges, shapes, colors, distances between the shapes, etc.). Those patterns will then inform a predictive model that is able to look at a new set of images and predict whether they contain cats or not, based on the model it has created using the training data.

Deep learning algorithms do this via various layers of artificial neural networks which mimic the network of neurons in our brain. This allows the algorithm to perform various cycles to narrow down patterns and improve the predictions with each cycle.

A great example of deep learning in practice is Apple’s Face ID. When setting up your phone you train the algorithm by scanning your face. Each time you log on using e.g. Face ID, the TrueDepth camera captures thousands of data points which create a depth map of your face and the phone’s inbuilt neural engine will perform the analysis to predict whether it is you or not.

What is reinforcement learning?

Reinforcement learning is an autonomous, self-teaching system that essentially learns by trial and error. It performs actions with the aim of maximizing rewards, or in other words, it is learning by doing in order to achieve the best outcomes. This is similar to how we learn things like riding a bike where in the beginning we fall off a lot and make too heavy and often erratic moves, but over time we use the feedback of what worked and what didn’t to fine-tune our actions and learn how to ride a bike. The same is true when computers use reinforcement learning, they try different actions, learn from the feedback whether that action delivered a better result, and then reinforce the actions that worked, i.e. reworking and modifying its algorithms autonomously over many iterations until it makes decisions that deliver the best result.

A good example of using reinforcement learning is a robot learning how to walk. The robot first tries a large step forward and falls. The outcome of a fall with that big step is a data point the reinforcement learning system responds to. Since the feedback was negative, a fall, the system adjusts the action to try a smaller step. The robot is able to move forward. This is an example of reinforcement learning in action.

One of the most fascinating examples of reinforcement learning in action I have seen was when Google’s Deep Mind applied the tool to classic Atari computer games such as Break Out. The goal (or reward) was to maximize the score and the actions were to move the bar at the bottom of the screen to bounce the playing ball back up to break the bricks at the top of the screen. You can watch the video here which shows how, in the beginning, the algorithm is making lots of mistakes but quickly improves to a stage where it would beat even the best human players.

Difference between deep learning and reinforcement learning

Deep learning and reinforcement learning are both systems that learn autonomously. The difference between them is that deep learning is learning from a training set and then applying that learning to a new data set, while reinforcement learning is dynamically learning by adjusting actions based in continuous feedback to maximize a reward.

Deep learning and reinforcement learning aren’t mutually exclusive. In fact, you might use deep learning in a reinforcement learning system, which is referred to as deep reinforcement learning and will be a topic I cover in another post.

10 Amazing Examples Of How Deep Learning AI Is Used In Practice?

You may have heard about deep learning and felt like it was an area of data science that is incredibly intimidating. How could you possibly get machines to learn like humans? And, an even scarier notion for some, why would we want machines to exhibit human-like behavior? Here, we look at 10 examples of how deep learning is used in practice that will help you visualize the potential.

What is deep learning?

Both machine and deep learning are subsets of artificial intelligence, but deep learning represents the next evolution of machine learning. In machine learning, algorithms created by human programmers are responsible for parsing and learning from the data. They make decisions based on what they learn from the data. Deep learning learns through an artificial neural network that acts very much like a human brain and allows the machine to analyze data in a structure very much as humans do. Deep learning machines don't require a human programmer to tell them what to do with the data. This is made possible by the extraordinary amount of data we collect and consume—data is the fuel for deep-learning models. For more on what deep learning is please check out my previous post here.

10 ways deep learning is used in practice

1. Customer experience
Machine learning is already used by many businesses to enhance the customer experience. Just a couple of examples include online self-service solutions and to create reliable workflows. There are already deep-learning models being used for chatbots, and as deep learning continues to mature, we can expect this to be an area deep learning will be used for many businesses.

2. Translations
Although automatic machine translation isn’t new, deep learning is helping enhance automatic translation of text by using stacked networks of neural networks and allowing translations from images.

3. Adding color to black-and-white images and videos
What used to be a very time-consuming process where humans had to add color to black-and-white images and videos by hand can now be automatically done with deep-learning models.

4. Language recognition
Deep learning machines are beginning to differentiate dialects of a language. A machine decides that someone is speaking English and then engages an AI that is learning to tell the differences between dialects. Once the dialect is determined, another AI will step in that specializes in that particular dialect. All of this happens without involvement from a human.

5. Autonomous vehicles
There's not just one AI model at work as an autonomous vehicle drives down the street. Some deep-learning models specialize in streets signs while others are trained to recognize pedestrians. As a car navigates down the road, it can be informed by up to millions of individual AI models that allow the car to act.

6. Computer vision
Deep learning has delivered super-human accuracy for image classification, object detection, image restoration and image segmentation—even handwritten digits can be recognized. Deep learning using enormous neural networks is teaching machines to automate the tasks performed by human visual systems.

7. Text generation
The machines learn the punctuation, grammar and style of a piece of text and can use the model it developed to automatically create entirely new text with the proper spelling, grammar and style of the example text. Everything from Shakespeare to Wikipedia entries have been created.

8. Image caption generation
Another impressive capability of deep learning is to identify an image and create a coherent caption with proper sentence structure for that image just like a human would write.

9. News aggregator based on sentiment
When you want to filter out the negative coming to your world, advanced natural language processing and deep learning can help. News aggregators using this new technology can filter news based on sentiment, so you can create news streams that only cover the good news happening.

10. Deep-learning robots
Deep-learning applications for robots are plentiful and powerful from an impressive deep-learning system that can teach a robot just by observing the actions of a human completing a task to a housekeeping robot that’s provided with input from several other AIs in order to take action. Just like how a human brain processes input from past experiences, current input from senses and any additional data that is provided, deep-learning models will help robots execute tasks based on the input of many different AI opinions.

The growth of deep-learning models is expected to accelerate and create even more innovative applications in the next few years.

3 Ways To Embrace Digitization To Improve Productivity

Going back to Economics 101, productivity is the stimulus that our economy needs. When productivity increases, wages and standards of living follow suit, causing the demand for goods and services to increase along with them. In a world where technology advances on a daily basis could we really be seeing a decline in productivity? You bet.

According to a recent report by McKinsey, “Productivity growth has fluctuated over time; it has been declining since the 1960s and today stands near historical lows.” In fact, between the years of 2010-2014, the total labor productivity growth stood at a negative 0.2%, compared to a positive 3.6% in 2000-2004 – just a decade difference.

There is hope, however. McKinsey believes that there is potential for the productivity levels to recover to at least 2 percent. But how? Thanks to the ever-growing realm of tech, we now have digitization and the digital transformation.

Let’s discuss how digitization and the transformation can both be the key to our productivity struggle.

Transformation Through Upskilling and Training

There is always talk about robots taking all our jobs. However, it is being proved to not be the case. Digitization and the transformation will impact both high-skill and low-skill jobs yet will create more in the long run for us all. However, those who are digital natives including millennials, Generation Z, and their kids will be the majority; those that have the skills necessary to perform these positions. To increase our productivity at ground level, we need to begin upskilling our current workforce and implementing training that fits.

Companies such as AT&T are beginning to see the true value in upskilling their employees. Scott Smith of AT&T put it this way, “You can go out to the street and hire for the skills, but we all know that the supply of technical talent is limited, and everybody is going after it. Or you can do your best to step up and reskill your existing workforce to fill the gap.” Since beginning their upskilling initiative, AT&T has reduced its product development lifecycle by 40% accelerated time to revenue. It’s an impressive feat that only upskilling and dedication can create.

During digitization and the digital transformation, your business will need to create a strategy for cybersecurity, artificial intelligence and more. How will you be able to manage these technologies? You must have a current talent base that can implement these tools once they hit your front door. Upskilling and training can completely change the game.

Digitization Through Diffusion

It is critical that digitization and technology are adopted by all enterprise, not just a select few, to boost productivity. Also called digital diffusion, McKinsey states, “Action is needed both to overcome adoption barriers of large incumbent business and to broaden the adoption of digital tools by all companies and citizens. Actions that can promote digital diffusion include: leading by example and digitizing the public sector, leveraging public procurement and investment in R&D and driving digital adoption by small and medium-sized enterprises.”

The digital transformation will have an impact on businesses that do not choose to adopt the up and coming technology. In fact, these businesses may suffer the consequences and be left behind. True digitization to boost our productivity will need to include all sectors and enterprise to make a difference. This means that large corporations will need to face their tech demons head-on and solve their adoption issues with strategy. Small businesses and mid-size enterprises will need to begin adopting technology to remain competitive.

Reinvention Through Strategy

McKinsey asks the question: “How do companies, labor organizations, and even economists respond to the challenge of restarting productivity growth in a digital age? Companies will need to develop a productivity strategy that includes the digital transformation of their business model as well as their entire sector and value chain.” Every change that must be made, every training strategy and move towards digitization is part of a larger digital transformation strategy.

When it comes to businesses that place emphasis on their digital strategy, “We found that more than twice as many leading companies closely tie their digital and corporate strategies than don’t. What’s more, winners tend to respond to digitization by changing their corporate strategies significantly.”

Businesses that are investing in digital transformation by changing their strategy are boosting their productivity through revenue growth and return on digital investment. In fact, McKinsey found in further research that 49 percent of leading companies, in revenue growth, EBIT growth and digital investment, are investing in digital more than their counterparts do.

McKinsey concluded that bold strategies win. And I agree. With a digital transformation strategy, strong levels of digital diffusion and upskilling of the workforce, we are sure to see the increase in productivity that is predicted. It is time now to embrace digitization more than ever, from the top of the ladder to the bottom. After all, our economy depends on it.

6 Ways To Make Smart Cities Future-Proof Cybersecurity Cities

By 2050, about 70% of the world’s population is expected to live in cities. Using the Internet of Things, analyzing lots of data, putting more services online—all herald the digital transformation of cities. Becoming digital, however, means a new life in the cybersecurity trenches.

There is no place like Israel to teach local government leaders how to make their cities and citizens cybersecurity resilient. Welcoming attendees from 80 countries to the Muni World 2018 event in Tel-Aviv, Eli Cohen, Israel’s minister of economy and industry, highlighted the fact that the country represents 10% of the global investment in cybersecurity. And it shares its expertise with others, including alerting 30 countries to pending cyber or terrorist attacks, Cohen said. (I was attending the event as a guest of Vibe Israel).

Cybersecurity is a prerequisite for the smart city, argued Gadi Mergi, CTO at Israel’s National Cyber Directorate. That means pursuing security, privacy and high-availability (having a cyberattack recovery plan, backup facility, cloud management, and manual overrides) by design. As other presenters discussed at the event (see the list of presenters below), smart cities must adjust and adapt to the requirements of the new cybersecurity landscape, characterized by:

The expansion of the attack surface with the introduction of new points of potential vulnerability such as connected and self-driving cars, and the Internet of Things (71% of local governments say IoT saves them money but 86% say they have already experienced an IoT-related security breach);

A wider range of attacker motivations, including ransomware (it was the motivation behind 50% of attacks in the US in 2017, with ransom payments totaling more than $1 billion) and hactivism (drawing attention to a specific cause, adding cultural and political dimensions to cyberattacks);

Increased consumer concern about personal data privacy and loss (30% of customers will take action following a data breach—demand compensation, sue or quit their relationship with the vendor);

Not enough people with the right expertise and experience (the much talked-about cybersecurity skill shortage is exacerbated in municipalities which find it hard to compete for scarce talent with organizations with much deeper pockets; this challenge becomes even more severe with the introduction of new approaches to cybersecurity involving new tools based on machine learning and artificial intelligence);

Insisting on fast time-to-everything (Agile is not agile enough) results in reduced quality of cybersecurity applications.

What’s to be done about meeting these challenges? Here’s a short list of priorities for leaders of smart cities worldwide, based on the presentations at Muni World:

Prepare for the worst - develop a protection strategy and emergency plans, and get outside experts to help;

Practice - training and testing and more training and testing and simulations;

Automate - implement a continuous adaptive protection, automate the process of detection and response, apply algorithms liberally, including AI and machine learning-based solutions;

Upgrade - keep up with attackers’ new methods and tools, improve the state of hardware and software including leveraging the cloud and big data analytics and invest in elevating the skill level of the people responsible for cybersecurity defense;

Share - raise public awareness, disclose your experiences, and exchange information with other local governments;

Separate and disinfect - insert a virtual layer between the internal network and the internet, allowing only for sending commands and showing display windows, and make downloadable files harmless by deleting areas where programs may exist or transform them into safe data, regardless if they are malicious or not.

In addition to Eli Cohen and Gadi Mergi, other presenters at Muni World included Jonathan Reichental, CIO, City of Palo Alto, California; Roy Zisapel, co-founder and CEO, Radware; Menny Barzilay, Co-founder and CEO, FortyTwo Global; Morten Illum, EMEA VP, Aruba/HPE; Takahiko Makino, City of Yokohama, Japan; Yosi Schneck, Senior VP, Israel Electric Corporation; and Sanaz Yashar, Senior Analyst, FireEye.

Tamir Pardo, the former Director of the Mossad (Israel’s national intelligence agency), also spoke at the event, comparing the cyber threat to “a soft and silent nuclear weapon.” There is no way to stop a penetration, he said, and there will never be a steady state for cyber security.

Meaning life in the cybersecurity trenches, for local governments and all other organizations, will continue to get very interesting. To quote FireEye’s Sanaz Yashar (who quoted President Eisenhower), “plans are nothing; planning is everything.”