For and about all those people working to build together a sustainable world through collective intelligence and agility. Knowledge to share and collective behaviours to adopt to build and foster a sustainable future for all of us. «The future is already here – it's just not evenly distributed» William Gibson, who invented the word «Cyberspace»
A few years ago, investors and startups were chasing “big data”. Now we’re seeing a similar explosion of companies calling themselves artificial intelligence, machine learning, or collectively “machine intelligence”. The Bloomberg Beta fund, which is focused on the future of work, has been investing in these approaches.
Computers are learning to think, read, and write. They’re also picking up human sensory function, with the ability to see and hear (arguably to touch, taste, and smell, though those have been of a lesser focus).
Machine intelligence technologies cut across a vast array of problem types (from classification and clustering to natural language processing and computer vision) and methods (from support vector machines to deep belief networks). All of these technologies are reflected on this landscape.
What this landscape doesn’t include, however important, is “big data” technologies. Some have used this term interchangeably with machine learning and artificial intelligence, but I want to focus on the intelligence methods rather than data, storage, and computation pieces of the puzzle for this landscape (though of course data technologies enable machine intelligence).
We’ve seen a few great articles recently outlining why machine intelligence is experiencing a resurgence, documenting the enabling factors of this resurgence. Kevin Kelly, for example chalks it up to cheap parallel computing, large datasets, and better algorithms.
Big companies have a disproportionate advantage, especially those that build consumer products. The giants in search (Google, Baidu), social networks (Facebook, LinkedIn, Pinterest), content (Netflix, Yahoo!), mobile (Apple) and e-commerce (Amazon) are in an incredible position. They have massive datasets and constant consumer interactions that enable tight feedback loops for their algorithms (and these factors combine to create powerful network effects) — and they have the most to gain from the low hanging fruit that machine intelligence bears.
Best-in-class personalization and recommendation algorithms have enabled these companies’ success (it’s both impressive and disconcerting that Facebook recommends you add the person you had a crush on in college and Netflix tees up that perfect guilty pleasure sitcom).
Now they are all competing in a new battlefield: the move to mobile. Winning mobile will require lots of machine intelligence: state of the art natural language interfaces (like Apple’s Siri), visual search (like Amazon’s “FireFly”), and dynamic question answering technology that tells you the answer instead of providing a menu of links (all of the search companies are wrestling with this).Large enterprise companies (IBM and Microsoft) have also made incredible strides in the field, though they don’t have the same human-facing requirements so are focusing their attention more on knowledge representation tasks on large industry datasets, like IBM Watson’s application to assist doctors with diagnoses.
On Monday, the #SocialLeader community once again met on Twitter to talk about an important aspect of modern leadership: Can – and does – leadership now come at us from different directions and in a variety of shapes and sizes? Are we seeing th [...]
Some of the process-related questions leaders ask during their interaction with front line workers include variations on “What is the standard?” and “What is the actual process?” and upon finding a gap “Why is there a gap?” and "What are you doing to close the gap?" These can be part
Claude Emond's insight:
3 «bad» excuses senior management uses for not consulting with their employees working in the «gemba» (the real place : http://en.wikipedia.org/wiki/Gemba ). Are you afraid of the gemba too? Maybe you should be more afraid of the consequences of not using the collective intelligence of your employees! Any comments?
Still amazes me that only $ is the focus. $ is a result, not the Key! Performance come from humans!!!. so maybe we should manage humans. HAPPY HUMANS = HIGH PERFORMANCE = GREAT FINANCIAL RESULTS AND GROWTH. Don't you think? :)
Si les ingrédients nécessaires à l'émergence de l'Afrique paraissent plutôt identifiables, la question de la méthode -comment s'y prendre pour réussir?- apparaît autrement plus ardue. Pour La Tribune, Abdelmalek Alaoui, spécialiste marocain de l'Afrique, PDG de Guepard Group, conseil auprès du comité exécutif du groupe Mazars et distingué Young Global Leader par le Forum de Davos, esquisse des réponses.
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