Joseph Bou-Younes's Website

  • Ramblings on No Code and the Permanent Revolution

    Since leaving Borealis I’ve spent time some time getting to know “no code.” Low Code Application Platforms (LCAPs), or “low” or “no code,” “No Code,” seem to have broken through. The promise is that non-programmers can point-and-click their way through building mobile/web Apps and deploy them straight to Google Play, the App Store, or a corporate app store. Last November Gartner assumed that “by 2024, three-quarters of large enterprises will be using at least four low-code development tools for both IT application development and citizen development initiatives” and “low-code application development will be responsible for more than 65% of application development activity.” Stuff is happening (waves hands).

    There is Nothing New Under The Sun

    We’ve been promised programming for non-programmers since electronic computers were invented. Just Google “4GL” or read the history of PowerBuilder. That said, as with most technologies a big enough difference in degree generates a difference in kind. So what’s different now is:

    1. Cloud and continuous deployment

    It’s now “normal” to have a developer deploy live code to production with a click. Ten years ago you’d still expect to have a sysadmin setup physical machines, flow code through a staging environment, whatever. Now, some 22 year old makes a commit, it runs through automated testing, and minutes later the code is live in multiple data centres for some or all end users. Magic.

    2. Every company is digital first

    Companies now, primarily interact with customers through digital channels. Banks, of course, still have branches, and retailers still have physical stores, but these are really just meatspace user interfaces that sit on top of software. Software ate the world. So anyone who works in a large organization who has some customer responsibility is now responsible for software, regardless of their stated job description.

    3. The data is sitting there

    Thanks to cloud, and some mind-numbing-but-vital data fabric building many “citizen developers” can now read-and-write important data in more or less real-time. If you layer in human-in-the-loop stuff like Figure 8, would-be developers are one click away from any bit of data that they could possibly need to build an app.

    4. Thinner IT organizations with an ever-growing dependence on outsourcing

    She who can get the most done, in the least amount of time, and claim the most credit will win. Low-Code lets you move fast as it takes the availability of specialized, highly-skilled specialized technical labour off the critical path.

    Oh you’ll still need specialized labour — people with a formal computing background who can untangle spaghetti code and keep user data out of unit 61398 but low-code lets you, as a middling business leader, move fast and break things (cringe). The time to proof value can be way shorter than with traditional programming — and in many cases you can throw dozens of 22 year olds from $consultingFirm to show traction and secure your bonus. Then you can get a proper budget in the next annual cycle, hire specialists, and build your empire.

    If you’re a BigCo manager you don’t need approval or budget to start a low-code project; you just need access to whichever LCAP(s) your IT and security people have approved. You can cobble things together with existing staff, get a bit of traction (or at least make a sexy demo) and then marshal resources for a real launch. It’s Lean Startup for Enterprise (TM).

    The point is, there are technical (cloud, digital first) and business (agility in the face of rigidity) factors that make Low-Code possible, and logical. It holds at least some promise of moving companies into a state of glorious Permanent (Digital) Revolution.

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  • Connecting Humans with What They Need

    A year ago I wrote that 2016 would be the year that consumer AI went mainstream. In one sense, I was wrong. The average consumer still doesn’t interact with an AI application on a typical day. Siri, Cortana, Google Home, and Allo are making inroads but still have small reach when compared to, say, Android or iOS as a whole.

    Look Deeper

    Looking back, I realize that my angle of attack was wrong. “Consumer artificial intelligence” will not mostly be about putting AI in end-user devices. Siri, chatbots, and other natural language interfaces are a piece of the picture. However, the really interesting stuff is happening just below the surface.

    Last week, The New York Times published a long, breezy piece about AI. It centered on the migration of Google Translate from “conventional software” to neural networks. The article is a wonderful ramble through the AI countryside and I highly recommend it.

    What was done to Google Translate is a great illustration of where the action is with AI right now. Rather than transforming how we interact with technology, AI is, instead, first transforming what happens behind the scenes in applications like web search and machine translation. This is analogous to what happened with other, historic new technologies.

    Horseless Carriages, then Cars

    In the early steam age, manufacturing plants were still laid out in the same way that they were when they were powered by water wheels. When workplaces were electrified, machines were still placed as though they had to be connected to a steam engine and desks were left near large windows, and so on. It takes time to learn how to apply a new technology. But we are learning.

    The Machine Intelligence Landscape

    For the past three years, Shivon Zilis and the team at Bloomberg Beta have been mapping the Machine Intelligence Landscape. 2016 saw an explosion of startups applying artificial intelligence to specific business problems. This is because it is easy to estimate, capture, and charge for the value an AI solution creates for a business. It is also relatively easy to identify and process training data.

    Because of this, AI is becoming ubiquitous in business software. As a result, two years from now you won’t talk about “AI SaaS companies,” or “AI technology companies selling to business” — you’ll just assume that every piece of software marketing to business incorporates AI appropriately, just as that software uses a relational database or runs on the internet.

    We’ll have optimized the heck out of what’s inside the little boxes of a business (sales, marketing, etc.) and will move on to the interesting stuff — creating new things that couldn’t happen without AI, and that blow up the boxes altogether.

    But Back to the Consumer

    This is where things get interesting; what happens when these new companies — with their blown up boxes, and their AI powered businesses — interact with consumers, directly through their devices? We were given a glimpse of this with Amazon Go a few weeks ago — a grocery store that uses machine vision and AI, along with other technologies, to end the checkout line. The thin end of the wedge were the Google Now and iPhone prompts that tell you when you need to leave to get to your best appointment, or warn you about a transit delay.

    A tough question: What do we even want?

    If we take a step further back — what do we, as consumers, ultimately want AI to do? Clearly, automating routine work and improving our safety is important. But then what? The answer is definitely not “generate more notifications on my phone.” Or “give me another awful pseudo-human to talk to,” one that’s just as frustrating as the typical Comcast customer-service representative.

    AI will break down barriers and connect people to what they need

    So now we truly begin to apply AI to the long project of connecting humans with the things that they value. The things they need to complete their day, raise their families, and run their lives. We need to create new connections from human needs, across time and space, going deep into the organizations that can satisfy those needs. Re-think our businesses, re-think our lives, embrace what is possible, and blow up the little boxes. To create revolutionary change with accelerating, incremental change.

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  • On Communicating

    6. Prepare your intent.

    A little preparation goes a long way toward saying what you wanted to say and having a conversation achieve its intended impact. Don’t prepare a speech; develop an understanding of what the focus of a conversation needs to be (in order for people to hear the message) and how you will accomplish this. Your communication will be more persuasive and on point when you prepare your intent ahead of time.

    2. Talk so people will listen.

    Great communicators read their audience (groups and individuals) carefully to ensure they aren’t wasting their breath on a message that people aren’t ready to hear. Talking so people will listen means you adjust your message on the fly to stay with your audience (what they’re ready to hear and how they’re ready to hear it). Droning on to ensure you’ve said what you wanted to say does not have the same effect on people as engaging them in a meaningful dialogue in which there is an exchange of ideas. Resist the urge to drive your point home at all costs. When your talking leads to people asking good questions, you know you’re on the right track.

    – Travis Bradberry, 8 Secrets of Great Communicators

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  • Beware the Apex Predators

    https://www.tumblr.com/welcometobusinesstown/126279658496/beware-the-apex-predators
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  • Why Consumer AI Goes Mainstream in 2016

    In October of 1994, Netscape released Netscape Navigator 1.0, the first commercial web browser. Over the next decade, the web went mainstream as it became increasingly usable.

    In October of 2011, Apple announced that Siri — the mobile personal assistant it acquired in 2010 — would ship on the iPhone 4S. Siri has continued to improve, as have Google Now, Amazon Echo, and a host of other solutions. 2016 will be the year that consumer AI goes mainstream.

    The Future Arrives

    For twenty years voice recognition had been “the future.” While Watson and Wolfram Alpha captured the attention of the press, they both had negligible impact on consumers. While the world obsessed over screen size and Angry Birds, Siri kept getting better.

    With WatchOS2 and an iPhone 6, Siri finally feels usable. I find myself tapping the phone less, and talking to my wrist more. How did this happen, and what can it tell us about the near future?

    • Experience effects. Like most machine intelligence, the more heavily Siri is used, the better it gets. Apple’s acquisition of VocalIQ, the world’s first self-learning dialogue system, should further increase the rate of improvement.
    • Moore’s law. AI problems are often bound by processing speed. CPUs double in performance every two years, creating a “natural” rate of improvement for AI systems. So, for the same CapEx, Apple now has four times as much processing power to throw at the problem. AI performance is further improved by algorithmic enhancements, like migrating processing work away from general-purpose CPUs and onto GPUs (graphical processing units), which are faster but more difficult to work with.
    • Apple built a search engine. Apple built a search engine to make it easy and worthwhile for app developers to extend Siri — without even knowing it. The Search API lets developers to describe their app to Apple (and Siri) with simple markup. Search is Apple’s way to evolve Siri into a tightly controlled platform.

    Siri is Now A Toy that Works

    Siri is now the main way I handle use cases where I can articulate a clear question (“what is the weather tomorrow?”) or command (“remind me to buy milk tonight”). While I emphasize ‘clear question’, the future will likely behold Siri’s ability to handle increasingly complex natural language questions that deliver an optimal solution to a problem. For example, “Given the tastes of my dinner guests, what meals should I prepare?”

    It Keeps Getting Better

    The ecosystem around Apple’s AI implementation is strengthening every day. Developers are exposing more and more functionality to Siri through the Search API. There are, however, bottlenecks imposed by Apple’s privacy policies that prevent it from having access to the rich user-generated datasets it helps create.

    The Kids Think It’s Normal

    And by “kids” I don’t mean “millennials,” I mean the toddlers running about my house. The older one now orders Siri around. She expects to be able to talk to a computer. Remember when kindergarteners suddenly expected all screens to be touch sensitive? Generational shifts like these are great leading indicators of what’s next.

    Conclusion: Next Year…

    By the end of next year, consumer AI will be everywhere. Operating systems will expose key features of installed applications or replace them altogether.

    Facebook M, Operator, WhatsApp, WeChat, Slack, Kik, and every service with a natural language interface is, at its heart, an AI platform. Algorithms can either establish a direct rapport with users or monitor what is being said in a privacy-sensitive way, collecting intelligence and offering assistance.

    For example, Apple will integrate Siri within Messages and Mail, Emu-style.

    …and Beyond

    Consumer AI will continue to improve by a factor of two every two years. This sustained, exponential improvement will bring startling results. Incremental projects will overtake attempts at big-bang disruption. Think less “Google Self-Driving Car” and more “Tesla Autopilot.”

    Incumbent players will accelerate the acquisition of consumer AI applications to bolster their teams, and to defend their positions. Nobody wants to have done to them what Google did to Yahoo.

    Updated on December 16, 2016 based on feedback from Nathan Benaich and Ahmad Nassri, as well as what I learned at the Machine Learning and the Market for Intelligence hosted by the @creativedlab and the University of Toronto. Inspired in part by Shivon Zilis’s excellent work in the space.

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  • Toronto Moonrise

    theeconomist:

    The moon rises behind the skyline of Toronto, Canada on November 25th 2015. Credit: Reuters /Mark Blinch

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  • Fixing our Unhealthy Obsession with Work Email

    “Creative thinking requires a relaxed state, the ability to think through options at a slow pace and the openness to explore different alternatives without fear.”

    – Fixing our Unhealthy Obsession with Work Email
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  • The single greatest danger

    The single greatest danger for a founder is to become so certain of his own myth that he loses his mind. But an equally insidious danger for every business is to lose all sense of myth and mistake disenchantment for wisdom. 

    -Peter Thiel, Zero to One
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  • Photo of graffiti about Stephen Harper
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  • The Compass and the Clock

    Our struggle to put first things first can be characterized by the contrast between two powerful tools that direct us: the clock and the compass.  The clock represents our commitments, appointments, schedules, goals, activities — what we do with, and how we manage our time.  The compass represents our vision, values, principles, mission, conscience, direction — what we feel is important and how we lead our lives.  In an effort to close the gap between the clock and the compass in our lives, many of us turn to the field of ‘time management.

    – Steven R Covey, First Things First
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