We’ve Reached the Tipping Point on Talent
Our customers tell us they are missing the critical technical skills that they need to grow and stay competitive in the 21st century—IT, network architecture, security, and automation. A lack of digital skills is the single greatest challenge to successfully implementing digital transformation. In fact, 69 percent of respondents to an IDC Worldwide Digital Leader Survey said their organization lacks the right people, knowledge, and technology to transform.
Some of the most valuable skills are the hardest to find. According to a report last year, 41 percent of CIOs expect to face a skills shortage in data science, business intelligence, and analytics, and more than a third see upcoming shortages in security and risk management. It now takes an average of 96 days to fill a job that requires expertise in cloud security, more than double the IT industry average.
It’s clear that companies will need to rethink their approach to this growing skills gap. Increasingly, leading organizations are partnering with third-party service organizations—such as Cisco Services—to fill in their missing capabilities while making the most of the talent they do have.
In case you ever wondered, details matter especially in project management
Latest troubled project report
Organization : US Department of Defense – USA
Project : Military aid
Outline : Forest pattern uniforms selected for desert type terrain
Date : Jun 2017 Cost : $28M
Jacques Cartier Champlain Bridge Corporation – Canada
Project type : Highway overpass construction
Date : Oct 2016 Cost : $11M CAD
Organization: Volkswagen Group (VW)
Project type : Vehicle emissions system
Project name : Unknown
Date : September 2015
Cost : Potential costs in the region of $18B
Full list here: http://calleam.com/WTPF/?page_id=3
The Brightest Brains of AI Meet at World Summit AI
On October 11–12 2017, IBM Watson AI XPRIZE lead, Amir Banifatemi, represented the prize and participated in two key panels at the World Summit AI event in Amsterdam. Amir took center stage in the ‘Humanitarian AI’ panel to discuss how AI can play a role in addressing humanitarian needs and benefit all segments of society. He also moderated the ‘Over the Edge AI’ panel, where two teams competing in the IBM Watson AI XPRIZE—aifred healthand lili.ai—discussed how they are trying to make breakthroughs in AI for the benefit of humanity.
“When the user views the mirror, the user sees a reflection from the mirror of illuminated objects in the scene and the transmitted images from the display device through the mirror, the transmitted images being perceived as part of the reflected scene,” the inventors say.
An interesting viewpoint from Wire on the rise and fall (?) of chatbots.
It was easy for M’s leaders to win internal support and resources for the project in 2015, when chatbots felt novel and full of possibility. But as it became clear that M would always require a sizable workforce of expensive humans, the idea of expanding the service to a broader audience became less viable.
Another challenge: When M could complete tasks, users asked for progressively harder tasks. A fully automated M would have to do things far beyond the capabilities of existing machine learning technology. Today’s best algorithms are a long way from being able to really understand all the nuances of natural language.
“We launched this project to learn what people needed and expected of an assistant, and we learned a lot,” Facebook said in a statement. “We’re taking these useful insights to power other AI projects at Facebook. We continue to be very pleased with the performance of M suggestions in Messenger, powered by our learnings from this experiment.”
In 2015, Google drew criticism when its Photos image recognition system mislabeled a black woman as a gorilla—but two years on, the problem still isn’t properly fixed. Instead, Google has censored image tags relating to many primates.
What’s new: Wired tested Google Photos again with a bunch of animal photos. The software could identify creatures from pandas to poodles with ease. But images of gorillas, chimps, and chimpanzees? They were never labeled. Wired confirmed with Google that those tags are censored.
But: Some of Google’s other computer vision systems, such as Cloud Vision, were able to correctly tag photos of gorillas and provide answers to users. That suggests the tag removal is a platform-specific shame-faced PR move.
Bigger than censorship: Human bias exists in data sets everywhere, reflecting the facets of humanity we’d rather not have machines learn. But reducing and removing that bias will take a lot more work than simply blacklisting labels.