Overview: Innovation Strategy

a policy paper on Artificial Intelligence and Machine Learning to help navigate some of the opportunities and challenges ... de-mystifying some of its fundamental concepts

for innovators who are tasked with problem solving and imagining the future, human curiosity and playfulness will always have the advantage.. . machines cannot yet have intuition, nor the ability to empathize, reframe problems and truly innovate.. . The robots may be coming for our jobs, but it is too early to call checkmate on human ingenuity.

It will still need human workers to review the machines’ output and make decisions AI can’t, but the greater need will be for the middle-level employees who have the experience to make judgment calls....[but] Where do those middle-level employees get that experience and judgment?

Corporations are taking pages from startups, to emulate the culture of a fast-moving smaller company.

illustrates the current limitations of artificial intelligence... a human being invisibly takes over, responding to your request as if the algorithms were still at the helm... to reveal how people would react to an omniscient virtual assistant, and to provide data that would let the algorithms learn ...progress has been harder won than expected...…

if everyone has the same information, the crowd is no smarter than a single individual... a diverse collection of opinions, especially including minority views, is crucial for creating a smart group... we have an innate desire to imitate our peers... it may also be because of a rational, profit-seeking motivation... The more complex the situation…

Venting my spleen a little over on Medium.

Everyone wants to test and learn, so long as it’s failing fast without the failure... to win the award for taking the risk, so long as it wasn’t risky... Innovation is sweaty, risky, terrifying and takes balls... Innovation is the opposite of what we are pretending the word means.

there is no obvious way to design such a system so that it could always explain why it did what it did... How well can we expect to communicate—and get along with—intelligent machines that could be unpredictable and inscrutable? ... a journey to the bleeding edge of research on AI algorithms... including a meeting with one of the great philosopher…

becoming a learning organization was nothing like winning a marathon ...cultivating an award-winning orchard... nurturing systems and culture to produce meaningful results... We embarked on a systematic and phased approach to cultivating the organization we all wanted.

there are a number of companies looking at ways to apply AI, machine learning and related concepts to recruiting... we see glaring limitations to applying machine learning and AI to screen candidates based on their CVs and social media profiles alone... CVs alone fail to capture individual performance and contribution, raw talent, actual competenc…

As ML algorithms solve bigger and more complex problems... training them can require massive amounts of pre-labeled data.... As recently as 2015, only large companies like Google, Amazon and Apple had access to the massive data ... The democratization of ML gives individuals and startups a chance... But access to data is only one way in which M…

innovation is a series of steps, and that innovators possess certain characteristics... Almost everybody [has] innovative traits... the ability to think abstractly, having deep and broad knowledge, curiosity, openness to risk, grit, and dissatisfaction with the status quo... seek out experiences to put those traits to use

People are remarkably bad at explaining what they do, or how they go about reasoning, but we manage to live in a world filled with people, anyway.

There’s no shortage of resources about Digital Transformation on my Hub, but ... I’m not sure I recommend all of them. This edition includes three I do

how hard is it for someone to put true artificial intelligence into a chatbot? ... here are the 50 lines of code that enable your bot to understand speech:

business gurus ... identify the “one true path” to success... when applied to the real world, their advice doesn’t apply as cleanly as they promise and they often contradict each other... Simple rules rarely apply to a messy world... one common theme ... Great innovators constantly seek to identify new problems.

Many famous scientists have something in common—they didn’t work long hours... lives were full and memorable, their work was prodigious, and yet their days are also filled with downtime... great students didn’t just practice more than the average, they practiced more deliberately... engaging with full concentration in a special activity to improv…

not necessarily easy to get buy-in from the organization... different parts of the organization are responsible for their own little bits so you can’t always do it within your own team.

throwing more data at problems won’t necessarily lead to progress... us to artificial general intelligence... there’s low-hanging fruit that deep learning’s good at  —  specific narrow tasks ... we’re not making progress on cognition and strong AI.... we can’t well interpret what deep learning is doing... it isn’t literally exactly memorization…

Federated Learning enables mobile phones to collaboratively learn a shared prediction model while keeping all the training data on device, decoupling the ability to do machine learning from the need to store the data in the cloud.

there’s plenty of reasons why negativity abounds online... Incivility is a basic human instinct that’s encouraged by anonymity and exacerbated by inequality... anger helps drive participation... anyone who benefits from trolling — whether it’s platforms themselves or populist politicians — have little reason to improve the tone of online chatting…

Enterprise systems and business processes are constructed... from an organization’s most strategic asset: subject matter expertise. ... doesn’t all sit neatly in some database or model. It sits in the spreadsheets and brains of the business analysts, the ... engineers, etc. Capturing and codifying this expertise to create more intelligent and auto…

guides you through the traffic of social networks and automatically creates data visualizations for exceptionally insightful discoveries, all on the cloud. Identify a topic and quickly get relevant content that shows you interesting relationships and patterns. You can easily take the pulse of your audience, gain greater visibility into a topic or …

intuitive ways for people with big ideas to start building AI tools without big expertise.

“agile” describes so many different work systems that it’s become an almost meaningless term... for IBM “the leaders have to be with the squads [his word for small teams] and the squads have to be in a location.”... Research suggests remote workers are more productive and log more hours than employees who work in the office, and for many companie…

as CEO... your power and privilege leave you insulated... from information that might challenge your assumptions and allow you to perceive a looming threat or opportunity. Ironically, to do what your exalted position demands, you must in some way escape your exalted position... at firms that are highly successful innovators, leaders are especiall…

a list of basic resources for marketers who want to learn more about AI. Here’s a quick roundup of some of the things I’ve contributed to and some blogs to subscribe to, as of early 2017.

Facebook today cannot exist without AI. Every time you use Facebook or Instagram or Messenger... your experiences are being powered by AI... Eventually, this “classifying” step—known as supervised learning—may become automated, as the company pursues an ML holy grail known as “unsupervised learning,” ... Facebook sees the visual cortex merging wit…