VR without obstructing goggles opens up big opportunities

So far, using VR goggles makes nearly everyone look rather stupid. No one sees what you sees, and you face is covered. Now, Google has found a way forward which opens up great venues. Just think about it: Soon you can wear VR glasses that look like ordinary glasses, but that feed you the information you need as a layer on top of what we refer to as reality today. Soon we can, just by looking a person, see a summary of their latest Facebook and LinkedIn posts, and see suggestions of questions to ask based on their current lives. Spooky for some, but deeply meaningful for others.

Google has figured out a way to let you see VR headset users’ full faces while they’re immersed in an experience – as if the headsets were invisible.

Source: Google’s new tech turns VR headsets invisible

Amazing 3D-printing progress: Siemens print gas turbine blades #AI

It was just a matter of time before we could see this: A 3D-printed metallic component which will be used in very harsh environments. In just a few years this could change how components are sourced, manufactured, or shipped (or, rather, not shipped).

Successful first full load engine test of Additively Manufactured blades.

Source: Additive Manufacturing: 3D printed gas turbine blades – Industry & Automation – Pictures of the Future – Innovation – Home – Siemens Global Website

The car I love to drive, a Citroën, about to test autonomous cars with ‘non-expert’ drivers

For years, I have driven a Citroën C5 because it is just so comfortable. Gladly enough, the Groupe PSA are now testing their self-driving vehicles with people like you and me. Looking forward to hearing more about this, since ordinary users probably give other kinds of feedback than the professional drivers. A step in the right direction on marrying technology with humans needs.

Groupe PSA is the first automaker to gain the right to test in this way on French public roads.

Source: Peugeot, Citroën to test autonomous cars with ‘non-expert’ drivers – Roadshow

Ford, GM, Audi, Mercedes, and Nissan taking the lead for autonomous cars #AI

It was just a matter of time, I guess. So many articles have been focusing on what is happening in Silicon Valley while staying blind to the obvious: The big car corporations will do what they can to stay as leaders, also for the autonomous cars.

Tech companies are getting realistic about the automotive segment.

Source: The race for autonomous cars is over. Silicon Valley lost.

Contextual understanding and empathy in humans can teach #AI a lot

Gladly, companies are aiming to humanize the technology so it can understand us better.

Emotional intelligence is the future of artificial intelligence: Fjord | ZDNet

Those injecting human-like emotional capability into artificial intelligence will emerge as the front-runners in 2017 and beyond, according to Fjord.

Source: Emotional intelligence is the future of artificial intelligence: Fjord | ZDNet

#AI in self-driving cars and trucks is racing, but are we as humans keeping up?

Below is an interesting view of how artificial intelligence in cars, and trucks, is moving forward quickly, while we as humans sometimes are left behind. For many engineers, it seems the higher up on the automatic scale we come, the better. But we must always take into account how we as humans will interact with all this automation, and especially when the automation shuts down:

“Taking back control of a self-driving car might be relatively quick, but taking the right action might take a lot longer.”

We need more people to reflect on these vital questions.

Not Fast Enough: Human Factors in AI Self-Driving Cars for Control Transitions – AI Trends

You are driving your car and suddenly a child darts into the street from the sidewalk. You see the child in the corner of your eye, your mental processesSource: Not Fast Enough: Human Factors in AI Self-Driving Cars for Control Transitions – AI Trends

Deloitte says: Focus on Machine Intelligence (MI), not just #AI

Deloitte just released their report Tech Trends 2017: The Kinetic Enterprise, where they highlight the expected of Machine Intelligence (MI). Yes, they think we have focused too much on Artificial Intelligence (AI), which they say is a subset of Machine Intelligence (MI). As they describe it, MI also includes machine learning, deep learning, cognitive analytics, robotics process automation (RPA), and bots.

Deloitte also sees three areas as driving the development of MI: Exponential data growth, Faster distributed systems, and Smarter algorithms. And as we can see in the image below, all the tech giants are doing acquisitions to be better at MI. Given the sizes of these companies, and how many applications they offer us privately and professionally, this will have an enormous impact on us the coming years.

Conventional wisdom holds that artificial intelligence is the next great horizontal technology that will unleash future waves of innovation. Yet AI is not a single type of technology. It takes many f

Source: Deloitte predicts machine intelligence, not mere AI, as a big trend for 2017 | VentureBeat | AI | by Blaise Zerega

How companies are preparing to use Artificial Intelligence #AI

Instead of letting artificial intelligence (AI) be something abstract that scientists spend time on, companies can already prepare to use these technologies. When Infosys asked 1600 companies on how they plan to invest in AI, they responded like this:

Interestingly, 53% aim at developing knowledge and skills and 43% aim at using it for building a strong culture (here called “ethos”). This means that already today there are ideas on how to transform work. Meanwhile, for many companies, AI is still a step into the unknown, as reported by ZDNet in “Artificial intelligence: How to build the business case”. In this article, people especially see value from analyzing big data quickly. In order for the data be useful, however, you run into legal and ethical challenges that need to be solved first. And it will for sure involve a big disruption for certain professions when AI enters their domains:

“Peers recognises AI could also help change the way lawyers work, yet he also expects a cultural challenge. Senior partners trust their associates to spend hours considering the details of legal documents. Trusting computers to undertake the same task in seconds presents a different form of dependence. It’s a big shift because the reputation of that lawyer and firm is on the line”.

One way to look at the upcoming opportunities of AI is by reading “5 global problems that AI could help us solve” from World Economic Forum (WEF).

They list five problems AI could solve:

1. Healthcare

2. Making driving safer

3. Transforming how we learn

4. Help us be smarter about energy

5. Helping wildlife

By applying the ideas from the above chart on the practical ideas such as those from WEF, we can set a direction. Therefore, entrepreneurs and intrapreneurs could ask questions like: “How could our development of knowledge and skills make driving safer by using AI?”. Let the ideas flow, and then later separate the crazy ones from the revolutionizing ones (there’s a fine line between these two).

Since AI still is quite early in the development, we still have time to answer such big questions. But we should not relax, I believe. AI is developing exponentially, meaning it will look radically different just one or two years from now. Therefore, take the time already now to think about how to make AI work for you.

Both images are taken from the linked resources, and belong to them

Autonomous or driverless cars, and will we buy them?

It is easy to get a bit sloppy when writing about future technology that will change our lives. For example, not using the distinction between autonomous and driverless cars correctly.  It turns out, however, that there are methods to knowing what we are talking about exactly.

Here 360 published The difference between autonomous and driverless cars, where they outline the six levels of automation. Many of us have level 1 cars today, with basic parking assistance and the like, while a few are at level 2. The levels also relate to the autonomous/driverless distinction:

“Starting at Level 2, you can call a car autonomous because it makes its own driving decisions. Following this argument, you can also call it self-driving, although the term seems more adequate for cars at Level 4 and 5.”

One reason these levels are not used in our everyday language, is that these kind of cars are still rare on the roads. This had led some researchers to focus on simulation what will happen, as in Majority of human drivers don’t ‘bully’ autonomous vehicles. One conclusion of their tests, is that:

“What we have found suggests that people find it hard to recognise automated vehicles and/or don’t yet understand how automated vehicles behave. In terms of their driving behaviour, they therefore treat them as they would any other vehicle. It is possible that this could change as exposure to autonomous vehicles increases, but more evidence is needed to substantiate this.”

The advancement in the auto industry with new cars being produced, and research like the one above, will help clear these questions as we move forward. A central concern is still attracting us as consumers. What if no-one wants a driverless car? says that consumers could be the biggest barrier to autonomy. We are so used to being in full control of our vehicles, that higher levels of automation might feel strange at first:

“Survey respondents overwhelmingly preferred Level Four autonomy, where a human still had the option of taking control of the car. Almost 10% vowed they would never buy a fully autonomous car while 40% wanted to keep driving information private, even if that made the roads less efficient.”

All images are fetched from the respective articles, and are their property

“Wanted: AI experts to build the robots that will replace them – Computerworld” #AI

Last month, global audit firm PwC posted a vacancy for an “AI Guru” at its Sydney office. The position required data analytics knowledge, experience in supervised and unsupervised machine learning, with basic programming and database ability an advantage.

Source: Wanted: AI experts to build the robots that will replace them – Computerworld