Managing the white-space | Leaving the smaller screens behind in iOS 11

One of the things I’ve noticed as the user of both an older and a newer iPhone is that the 4.7″ screen that is on the iPhone 6/7/8 is now the baseline standard being used for iOS design decisions.

In iOS 11 Apple have made a number of design decisions that increase the amount of screen being used by items.

In the AppStore, as an example, the icons have got bigger and the titles have got bigger, so that the number of apps you see in the Update section have reduced and the titles are often truncated on a 4″ iPhone 5/5S:

20171003_104203000_iOS
AppStore Updates on the iPhone 5S 4″ Screen with iOS 11.

Another example of the design choices being made is the lock screen and associated notifications. If you have a clock on your lock screen and you are playing some audio then notifications are almost useless because you only get part of the first notification without scrolling:

Lock Screen
Lock Screen on the iPhone 5S 4″ Screen with iOS 11

Screen design decisions are a balance between content and white-space, white-space is the space between the content. Good design is defined by the white-space more than the content. That’s where the iOS 11 design decisions are being driven from, as screens have got bigger on the iPhone 6/7/8 (4.7″) and the 6/7/9 Plus (5.5″) Apple are increasing the amount of white-space so that the design stays good on those devices.

Anyone who has used a corporate application will know how awful it is when white-space is ignored and content is crammed on to screens. Apple could have used the extra screen space on the newer iPhone models to squeeze in more content, which I’m sure they’ve done, but they’ve balanced it with an increase in white-space. Those design decisions mean that the content on the 4″ screen feels like it’s a bit too spaced out.

Humans and Robots: Seeing Robots, Warring Robots and Dancing Robots

One of the core skills we have as humans is the ability to recognise and recognise things that we see. The ability for robots to do this has advanced significantly in recent year as the TED Talk by Joseph Redmon demonstrates:

As robots continue to gain skills a number of people are advocating that the United Nations should ban robots that kill:

Lethal autonomous weapons threaten to become the third revolution in warfare. Once developed, they will permit armed conflict to be fought at a scale greater than ever, and at timescales faster than humans can comprehend. These can be weapons of terror, weapons that despots and terrorists use against innocent populations, and weapons hacked to behave in undesirable ways. We do not have long to act. Once this Pandora’s box is opened, it will be hard to close.

We therefore implore the High Contracting Parties to find a way to protect us all from these dangers.

An Open Letter to the United Nations Convention on Certain Conventional Weapons

We have a log history of weaponising technology advances, perhaps even as long as human history. Once you remove humans from the field of war the moral needs change significantly. What’s to stop an ever escalating conflict when there is limited moral need to stop?

If warring robots is a scary thought, how about dancing ones. Guinness world records recently published this video of dancing Dobi robots, 1069 in all:

Personally I think that this is quite scary.

Humans and Robots: Having an off-grid alternative and self-flying planes.

Some of the people closest to the ongoing robotic revolution have looked and decided that it’s time to have another alternative.

Until a couple of years ago, Antonio Garcia Martinez was living the dream life: a tech-start up guy in Silicon Valley, surrounded by hip young millionaires and open plan offices.

He’d sold his online ad company to Twitter for a small fortune, and was working as a senior exec at Facebook (an experience he wrote up in his best-selling book, Chaos Monkeys). But at some point in 2015, he looked into the not-too-distant future and saw a very bleak world, one that was nothing like the polished utopia of connectivity and total information promised by his colleagues.

“I’ve seen what’s coming,” he told me when I visited him recently for BBC Two’s Secrets of Silicon Valley. “And it’s a big self-driving truck that’s about to run over this economy.”

Silicon Valley luminaries are busily preparing for when robots take over

Most of the reported opinions on the future represent our future as if we are at a fork in the road with one way leading to a future Utopia and the other leading to a Dystopia. I’m sure that there are plenty of opinions that are somewhere in the middle but they tend not to get to much air time probably because it’s not very good copy.

A middle road is the most likely outcome with part of Utopia mixed with parts of Dystopia. I’m currently listening to a long book on the history of England and one of the things I’m learning from it is that good times and bad times generally live together side-by-side.

One area that has already seen significant automation is air travel. The pilot may be ultimately in charge but the systems available to them make them mostly redundant for most of the journey. yet, there is something settling about knowing that there is a human at the front making sure everything is going well. How would you feel about travelling a plane without a pilot?

UBS analysts expect the effort to familiarize the public with commercial self-piloting crafts will begin at that 2025 target date with autonomous cargo planes, which could demonstrate how the systems can safely fly from point A to B without a hitch. A next step could be to remove pilots gradually, shifting from a two-person cockpit to one person monitoring the system before phasing out humans entirely. 

Pilotless planes might be here by 2025, if anyone wants to fly in them

2025 isn’t very far away, and that’s the estimate for a start. i expect the transition period to be long.

The Huge Failure | Dilbert on Open Plan Offices

I’ve really enjoyed the recent series of cartoon from Scott Adams on open-plan offices:

Some of the comments on these cartoons are just as fabulous:

Let’s not forget that cubicles were a massive failure before open plans managed to out-fail them.

Open spaces are supposed to invite the open flow and exchanges of Ideas. And they do, ideas like……”How bad traffic was this morning?….Did you catch the game last night?….how was your weekend?…..etc” Maybe some work topics might get discussed

Working environments is a very emotive subject and rightly so. Many of us spend more time at work than we do at home and we want to be productive.

What fascinates me is that many organisations spend huge amounts of money creating something that people don’t want.

Here’s something I wrote earlier: Productivity and place: Where are you most productive?

Humans and Robots: The AI Over-hype and Sorting Lego

Last week I was on a remote island in the Outer Hebrides, we had WiFi at our cottage but it was slow, we didn’t have any mobile signal. There was a mobile signal in the nearby town, 3 miles away, but again, that was slow and covered a small portion of the island. It was a great reminder of how much we take connectivity for granted and that for much of the world that assumption is invalid.

Whilst I was away though there have been a number of AI, Machine Learning and Robotics related things happening:

As seems to be the case for all technologies a point is reached where people need to talk about the negative aspects including the hype-levels of the current hot tech. That has been the case this last week with MIT and others running stories. The MIT Technology Review one is looking specifically at IBM Watson and the perceived rate of progress it is making in healthcare. Progress in any technology is rarely a smooth ride and some high visibility failures are normal.

The MIT Technology Review has also been looking at the progress GE are making by using AI and Machine Learning. GE is going through a huge transformation that will embed advanced technologies and robotics into many of its products. This transformation is also radically changing the way people work with people taking on what would have previously been two separate roles. The role change is one that is already happening and I’ve got an post brewing on that.

The Guardian is the latest organisation to do a round up of the research and current thinking into the impact of automation on jobs:

“in the last 60 years automation has only eliminated one occupation: elevator operators.”

I’m not sure that’s really true, but I get the point. Aside from the statistics the core question people want to know the answer to is: “what can I do to prepare?” It’s not an easy question to answer, the only sure thing is that change is going to happen and humans have adapted to change for hundreds of thousands of years. That ability to adapt is what’s going to be key in the future, one way of being adaptable is to diversify and be able to take on multiple roles.

If you want to know more about Machine Learning then this nice Visual Introduction from R2D3 will get you started.

Jacques Mattheij had a dilemma, how to sort 2 metric tonnes of Lego which he did with some Lego (what else?), some hardware, python and a neural network. Although there won’t be many people with 2 metric tonnes of Lego I’m sure that many of us would love to be able to sort the boxes that do exist.


A Reality Check for IBM’s AI Ambitions via MIT Technology Review

None of those companies has garnered anywhere near the attention that Watson has, thanks to its victory on the television quiz show Jeopardy! in 2011 and zealous marketing by IBM ever since. But lately, much of the press for Watson has been bad. A heavily promoted collaboration with the M.D. Anderson Cancer Center in Houston fell apart this year. As IBM’s revenue has swooned and its stock price has seesawed, analysts have been questioning when Watson will actually deliver much value. “Watson is a joke,” Chamath Palihapitiya, an influential tech investor who founded the VC firm Social Capital, said on CNBC in May.


General Electric Builds an AI Workforce via MIT Technology Review

When Jason Nichols joined GE Global Research in 2011, soon after completing postdoctoral work in organic chemistry at the University of California, Berkeley, he anticipated a long career in chemical research. But after four years creating materials and systems to treat industrial wastewater, Nichols moved to the company’s machine-learning lab. This year he began working with augmented reality. Part chemist, part data scientist, Nichols is now exactly the type of hybrid employee crucial to the future of a company working to inject artificial intelligence into its machines and industrial processes.


What jobs will still be around in 20 years? Read this to prepare your future by The Guardian.

Today’s technological revolution is an entirely different beast from the industrial revolution. The pace of change is exponentially faster and far wider in scope. As Stanford University academic Jerry Kaplan writes in Humans Need Not Apply: today, automation is “blind to the color of your collar.” It doesn’t matter whether you’re a factory worker, a financial advisor or a professional flute-player: automation is coming for you.


A visual introduction to machine learning by R2D3

In machine learning, computers apply statistical learning techniques to automatically identify patterns in data. These techniques can be used to make highly accurate predictions.


Sorting 2 Metric Tons of Lego by Jacques Mattheij

Computer skills to the rescue! A first proof of concept was built of – what else – lego. This was hacked together with some python code and a bunch of hardware to handle the parts. After playing around with that for a while it appeared there were several basic problems that needed to be solved, some obvious, some not so obvious. A small collection:

Humans and Robots: Is your job doomed? Your robot chauffeur is waiting.

How close are the robots? They’re coming, but how far away are they? In 2013 Carl Benedikt Frey and Michael A. Osborne did the analysis for the US. The aptly named WILL ROBOTS TAKE MY JOB? site has made this analysis available alongside other data. The data set is very broad from shoe and leather workers (robots are watching) to personal care aides (robots are watching) to model makers, metal and plastic (doomed) to software developers, systems software (no worries).

Each of the roles and the analysis undertaken is debatable and a 2017 perspective almost certainly changes many of them, but it’s still a fun exercise to think through the impact of the robots on the many roles that people undertake.

The answer for Taxi Drivers and Chauffeurs is that your job has an 89% probability of automation and that the robots are watching.

I picked on Taxi Drivers and Chauffeurs because it’s been an interesting week for car technology.

Yandex which is Russia’s equivalent of Google and Uber has joined the race to create an autonomous vehicle with a project named Yandex.Taxi aiming for Level 5 autonomy (the highest level of autonomy defined by the SAE) also known as Full Automation.

They already have a demo available:

Cadillac are testing technology that is further integrating vehicles into the infrastructure in which they operate with vehicle-to-infrastructure (V2I) communication. This technology is also being developed to allow cars to talk to each other. In it’s latest announcement Cadilac are demonstrating how a vehicle would talk to traffic lights to enable a driver to know when the lights are going to change. At one level I think that this is a great idea, at another I can see all sorts of challenges. For me the greatest challenge is nearly always the humans, we have a wonderful ability to outsource our responsibilities to the technology, the smarter the technology becomes the lower our feeling of responsibility will become “Sorry officer, I ran the red light because my car failed to stop.”

The robots are definitely watching.

But how far can the robots go? When will their intelligence overtake human intelligence? When will we reach singularity and what will its impact be? That’s the question posed by a colleague Annu Singh:

now’s the time to think about and prepare for this tomorrow, before the limits of human intelligence startle us like a soft whisper.


Yandex.Taxi Unveils Self-Driving Car Project via Yandex

The driverless car incorporates Yandex’s own technologies some of which, such as mapping, real-time navigation, computer vision and object recognition, have been functioning in a range of the company’s services for years. The self-driving vehicle’s ability to ‘make decisions’ in complex environments, such as busy city traffic, is ensured by Yandex’s proprietary computing algorithms, artificial intelligence and machine learning.


Cadillac tech ‘talks’ to traffic lights so you don’t run them via Mashable

Cadillac tested out the V2I system by rigging two traffic signals near the GM Warren Technical Center campus to send data to its demo CTS vehicles. The automaker said the stop lights were able to use Dedicated Short-Range Communications (DSRC) protocol — which is the same system used for inter-car V2V communication — to send data to the cars about when the light would turn red.


Singularity in AI: Are we there yet? via DXC Blogs

While we may not be at the point of singularity yet, the growing capability of AI to make decisions, learn and correct its own decision-making process does seem to raise moral, ethical, social and security concerns. Consider the dilemmas being confronted now around self-driving cars. Insurance companies are questioning who owns the liability and risks, who carries the insurance policy. Developers are faced with unimaginable decisions about whose life gets saved in a deadly collision.

Humans and Robots: AI in Retail, Automotive, Weather and the Newsroom

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.