If I could think of a way to present it I would create a chart showing the various predictions for job creation and job losses that the robots are going to cause. One thing we can be sure about nearly all of these predictions is that they are likely to be wrong in detail, but correct in concept.
The latest prediction is one for retail which is using a World Economic Forum figure of 30%-50% of retail jobs being at risk from known automation capabilities. The challenge with this figure for most developed economies is that there are more people employed in retail than in manufacturing and many of us know the repercussions of the manufacturing switch, the predicted change is even greater than that experienced by manufacturing.
You might think that retail is at risk because it is easy to automate? But what about journalism? Earlier this month Google brought together a number of journalists to talk about the impact of AI in the newsroom. This meeting was discussing a report by the Associated Press “Report: How artificial intelligence will impact journalism”. Google were highlighting their Google News Lab which was it developed “to support the creation and distribution of the information that keeps us all informed about what’s happening in our world today—quality journalism.” Fake news has, of course, been a huge subject recently, I’m not so much concerned about outright fake news, that’s pretty easy to check, I’m more concerned by the potential for AI to create narrow news where it’s only the statistically high ranking items that become news.
Google were also highlighting their prowess at automatically classifying video content which they will soon be making available via Google Cloud Video Intelligence API. Classification of content is a massive issue for news organisations and having a machine do it for you has to be a winner.
In a more specific case for AI the UK Met Office has been talking about its use of AI to help predict the weather, something that’s something of an obsession for this island nation. This is underlined by the Met Office being one of the UK’s largest users of super-computing.
The impact of technology in the automotive business was recently underlined as Ford replaced its CEO with the person who was heading up their self-driving car business. Most of the content in this article is in the video.
And finally, anyone want an autonomous robot security guard with a built-in drone?
Retail Automation: Stranded Workers? Opportunities and risks for labor and automation by IRRC Institute (pdf)
The retail landscape is experiencing unprecedented change in the face of disruptive forces, one of the most recent and powerful being the rapid rise of automation in the sector. The World Economic Forum predicts that 30-50% of retail jobs are at risk once known automation technologies are fully incorporated. This would result in the loss of about 6 million retail jobs and represents a greater percentage reduction than the manufacturing industry experienced. Using Osborne and Frey study with the Bureau of Labor Statistics, the analysis suggests that more than 7.5 million jobs are at high risk of computerization. A large proportion of the human capital represented by the retail workforce is therefore at risk of becoming “stranded workers.”
Report: How artificial intelligence will impact journalism via AP Insights
Streamlining workflows, automating mundane tasks, crunching more data, digging out insights and generating additional outputs are just a few of the mega-wins that can result from putting smart machines to work in the service of journalism.
Innovators throughout the news industry are collaborating with technology companies and academic researchers to push the envelope in a number of related areas, affecting all points on the news value chain from news gathering to production and distribution.
AI in the newsroom: What’s happening and what’s next? via Google
“There is a lot of work to do, but it’s about the mindset,” D’Aniello said. “Technology was seen as a disruptor of the newsroom, and it was difficult to introduce things. I don’t think this is the case anymore. The urgency and the need is perceived at the editorial level.”
Announcing Google Cloud Video Intelligence API, and more Cloud Machine Learning updates via Google
Cloud Video Intelligence API (now in Private Beta) uses powerful deep-learning models, built using frameworks like TensorFlow and applied on large-scale media platforms like YouTube. The API is the first of its kind, enabling developers to easily search and discover video content by providing information about entities (nouns such as “dog,” “flower” or “human” or verbs such as “run,” “swim” or “fly”) inside video content. It can even provide contextual understanding of when those entities appear; for example, searching for “Tiger” would find all precise shots containing tigers across a video collection in Google Cloud Storage.