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Machine learning versus AI: what’s the difference?

Thanks to the likes of Google, Amazon, and Facebook, the terms artificial intelligence (AI) and machine learning have become much more widespread than ever before. They are often used interchangeably and promise all sorts from smarter home appliances to robots taking our jobs.

The UK has a new AI centre – so when robots kill, we know who to blame The UK has a new AI centre – so when robots kill, we know who to blameArtificial Intelligence 12 Oct 2016.

But while AI and machine learning are very much related, they are not quite the same thing. AI is a branch of computer science attempting to build machines capable of intelligent behaviour, while 
Stanford University defines machine learning as “the science of getting computers to act without being explicitly programmed”. 

You need AI researchers to build the smart machines, but you need machine learning experts to make them truly intelligent.

Source: Machine learning versus AI: what’s the difference?

Don’t think big! — Prototyping: From UX to Front End

One of the biggest mistakes you can make in your creative project is to pick a topic which is too big.As an artist and designer I keep making one mistake time and again. So if this article sounds like me giving advice to other creatives… it is actually an attempt to keep myself from making this mistake yet again.

Big topics often lead to small results, small topics foster great results.And here is why: Your project is limited by the time and energy you have.

Source: Don’t think big! — Prototyping: From UX to Front End

Email won’t go away any time soon | L’email ne s’en ira pas de sitôt | plerudulier

How many times have I read how dreadful email is killing business, how much time was wasted just to be able to empty one’s inbox; I may even have contributed every now and then.

Email is to remain for quite some time simply because of the way we work. If you have ever viewed presentations or tutorials of Slack which is meant to literally kill emails you may have realized that the demo scenario is systematically a unique project everybody seems to be working on at the same time. In the real life it doesn’t work that way : everybody is assigned to work on sets of projects which one rarely shares with anybody else.

Source: Email won’t go away any time soon | L’email ne s’en ira pas de sitôt | plerudulier

Innovate they ask; what’s in it for me I reply • ©[le-rudulier.eu]

Innovation seems to be my new entity’s new motto, there is even a team dedicated to it. As I changed position in the course  of this year I kept receiving, for a couple of months, news from my former entity where, what a coincidence, innovation is also emphasized.

I’m all for innovation being part of everyone’s scope of activity, I even wrote about purposely regularly dedicating a decent amount of time, to it . Making a common goal, a commitment that is shared by many, can only be more productive.

Of course, in order to achieve success, a bonus of … 500€ (maximum) was decided. That makes me cringe; excuse me but that’s a petty amount of money. It would be nice to have it in the pocket but hardly enough to make me seriously spend a reasonable amount of time for a good idea, if any thoughtful innovation is to be expected.

You give a little you get a little.

continue here …

via Innovate they ask; what’s in it for me I reply • ©[le-rudulier.eu].

Managing Your Mission-Critical Knowledge • ©[hbr.org]

When executives talk about “knowledge management” today, the conversation usually turns very quickly to the challenge of big data and analytics. That’s hardly surprising: Extraordinary amounts of rich, complicated data about customers, operations, and employees are now available to most managers, but that data is proving difficult to translate into useful knowledge. Surely, the thinking goes, if the right experts and the right tools are set loose on those megabytes, brilliant strategic insights will emerge.

Tantalizing as the promise of big data is, an undue focus on it may cause companies to neglect something even more important—the proper management of all their strategic knowledge assets: core competencies, areas of expertise, intellectual property, and deep pools of talent. We contend that in the absence of a clear understanding of the knowledge drivers of an organization’s success, the real value of big data will never materialize.

Yet few companies think explicitly about what knowledge they possess, which parts of it are key to future success, how critical knowledge assets should be managed, and which spheres of knowledge can usefully be combined. In this article we’ll describe in detail how to manage this process.

via Managing Your Mission-Critical Knowledge – HBR.

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