When the mapping company I worked for was sold, at least partly on the premise that Google was so dominating the industry that there was no market for the sort of data we were providing, I was surprised and a little disappointed. If I'd had the money I'd have considered buying it myself.

Reading the latest editions of magazines like Intertraffic World, businesses are scrambling over themselves to come up with new technologies to provide IoT solutions to compensate for the lack of data we had already collected in our country, such as where the edge of a road is, which is frequently obscured during storms or other conditions.

Here, (previously Navteq), has announced that they are investing millions into a new machine learning institute to "crunch geolocation data on an industrial scale" a great data-mining metaphor. That was one of the challenges when we were collecting data, the constant backlog of hard drives full of amazing information that we didn't have the processing power to crunch for the information we wanted. And of course machine learning and AI adds a whole new dimension of interpreting the data we wanted and other data that we didn't realise we had a use for, although we had an inkling.

Where we wanted to read traffic signs, I'm sure these new systems will augment it and enable the sort of navigation that can say 'turn right at the McDonald's' instead of "in 800 meters".

In recent times using data mining and machine learning, I learned that there is so much more information available, particularly relationships between data, people and transport movements that we have barely though of querying. Patterns that initially might not seem that relevant and that required analysts to explore for meaning. As more and more of that information becomes clear, companies like Here will be able to extrapolate it on a global scale.

The combination of road data, with Real Time Traffic Information, construction of new businesses, new residential subdivisions and artificial intelligence is light years beyond using sites like Fiverr to pay someone to go and confirm that an intersection has changed.

Here talks about 'Self Healing Maps' which is a long way from trolling council files on new road approvals. I just look at the confusion where a new road and a large roundabout has appeared on my route to the city. It has Uber and taxi drivers befuddled because its not on their maps and they don't know which way straight ahead is. Google doesn't have it sussed even as a proposed roundabout nor the new road connected to it.

Companies are evolving, with Here seemingly more focused on licensing data sets and in future ways to interpret what is happening on those roads, beyond the information they are already receiving from DoT's and other agencies. I'm sure they are also keen on partnerships to share what is being learned at least with organisations who have nothing to lose from sharing it. Insurance companies might now want to share risk related data, but I don't see why cities wouldn't want to share learning with each other, which would have dramatic cost benefits.

New businesses are evolving that most people wouldn't even have thought of, like the start-up GroundLevel Insights which will be delivering location intelligence and data analysis for the Cannabis Industry. Who would have thought? I'll tell you who, the awesome Asif R Khan aka "The Lord of Location", Founder and President of the Location Based Marketing Association. I hear that he is seeking investment in this new project.

Given his pedigree, knowledge and expertise in this space and the growing number of states legalising marijuana, I suspect this could be a winner. I recently wrote a song called Time Machine which I introduced on my new Twitch channel yesterday and asked people, if they had a time machine, what would they do with it? Would they be going back and buying shares in Amazon, Microsoft and Apple. The challenge today is finding the next big thing. This could be one of those. Weed has been around for hundreds of years, but legalising it changes everything. It seems to link very closely to technologies used in the forestry industry, except the market for this product in semi-raw form is enormous, the scale and the speed from seed to market is going to see it become one of the fastest growth industries in history. The tax income alone is likely to be almost unprecedented.

Back to Here, they recently launched SoMo, an app designed to support ride-share. I haven't seen it, but I have supported many startups in this space in the past and very few were able to deal with the technical vagaries of day of the week patterns, time of day, school holidays and other factors that impact on travel like the weather; and the information was not readily available. It's all about timing. I would suspect that the new Institute for Advanced Research in Artificial Intelligence that Here has committed to, will demystify and make available patterns of information that will make these solutions attractive through timeliness, information and accuracy that past solutions could only dream of.

Like Kahn's GroundLevel, information coming from these AI's won't just support driverless cars and smart cities, they will provide support for industries such as insurance, advertising, business efficiency planning, which is why the new consortium announced last week between TomTom, Microsoft and Moovit, heading in the same space are all focused on harnessing billions of location data points, real and virtual, real-time and historic.

It's great to see this is happening, because concurrent to this, hundreds of companies are developing location based sensors looking at everything from identifying where the road side and lane markings are at all times including during winter storms, to the location of vacant curbside car parks. Through the IoT, they will be able to feed into this rich data in real time.

What a great time it will be right now for newly graduating data scientists and business analysts with an understanding of deep learning. Good ones will be worth their weight in BitCoin.

What does it all mean in English? It means that cities can develop the capacity to run easily and smoothly without huge investment into expensive infrastructure. We as citizens will be able to make quick smart decisions on how to get from A to B, about where best to live, work and play based on current and constantly changing infrastructure. The same applies to business and government. So many decisions that today are based on heuristic bias, will be based on systems in the cloud that combine knowledge with analysis, which we as individuals don't need to understand.

The average citizen will have a much more rewarding life, with less time wasted. They won't need to understand about sensors and controls. They will simply be able to ask questions of their watch or their mobile and get simple directions. If you want to be in that place in 20 minutes, grab the Lime scooter from up the road, take it to the bus station, catch a bus and the journey will cost you $4.50. Forget about the car, you can't make it in time.

We will benefit from this data despite car manufacturers wanting to own their own data; and open source or economically accessible data will flow like water, and hopefully so will our transport systems. More and more these days I feel like I am watching a science fiction novel or movie from the inside. I love it.