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Computing’s Next Big Transformation – Semantic Intelligence

by | Oct 3, 2014 | Business Trends

Thomas Frey Futurist speaker computings next big transformation semantic intelligence

I had great difficulty completing this column. This is partly due to the complex nature of the technology and partly because its implications may indeed be so far reaching that I’ll sound over-reaching in describing it.

Several companies may find what I’m describing to be rather disturbing. It’ll be disturbing because this technology is on the verge of undermining most, if not all, of their product development plans.

For two nights this week I was immersed in understanding the foundational shifts about to occur inside the software development industry, and this work is all taking place inside a tiny company called Mindaptiv located in Innovation Pavilion in the Denver Tech Center, a hub of startup activity in Colorado.

With a core team of true believers on staff that filled the presentation room, the company’s CEO, Ken Granville, and chief technology visionary, Jake Kolb, took our team from the DaVinci Institute through a series of demonstrations and discussions to grasp the potential of what they are on the verge of unleashing.

Working from inside his secluded geek lab in Boston, Jake started this journey in 2011 by asking the basic question, “What if software didn’t have to be written?”

As most developers know, scripting a thousand lines of new code can be a very painful process. So what if a computer could simply recognize objects and you could just tell this JARVIS-like machine what you wanted it to do with them?

Over the past three years, that’s exactly what Jake and Ken have been building, a kind of “Ironman Room” of spatially capable objects that can be directed both verbally and through gestures with symphony-like precision. Even though they’re only partially there, it’s the kind of technology that would make Tony Stark proud.

Rest assured, I only know a few of the tricks this duo has up their sleeves, but we’re all about to become part of something much bigger than some new gadget we can all carry around in our pockets. No, this one is a game changer on steroids, and here’s why.

History Of Transformational Computer Technologies

Computer technology has gone through several fundamental shifts since they were first invented.

  1. 1944 – ENIAC: The grandfather, where Digital Computers began
  2. 1964 – IBM 360: Start of the Mainframe Computing era
  3. 1974 – Altair 8800: Start of the Personal Computing era
  4. 1990 – Tim Berners: Beginning of the World Wide Web
  5. 2007 – iPhone 1: Start of the Mobile Computing era
  6. 2015 – Mindaptiv: Entering the Semantic Intelligence era

Admittedly this is a gross oversimplification of the biggest transformations in computers. I could have included many other significant shifts ranging from the introduction of Browsers, to Search Engines, to Open Source, to P2P, to Cloud Computing, and much more.

Without a doubt, all of these elements have contributed to the evolution of today’s highly nuanced improvements leading to today’s sophisticated computer technologies.

But on a zero to ten scale for rating tectonic shifts on the Richter Scale of computing, Sematic Intelligence is drawing lines on parts of the chart that haven’t ever been written on before.

Semantic Intelligence Explained

We use our devices such as laptops, tablets, and phones to convey meaning. We talk on the phone, write and read text, emails, blogs, news, look at and send pictures and videos. We do this because these inputs and outputs symbolically represent objects with behaviors and attributes that make sense to us as humans.

We don’t see pixels; we see words that our mind converts into pictures. We don’t see all the tiny squares, circles, and rectangles on the screen, but rather what they represent. In video, we don’t see still images or individual frames. Instead, we see the fluid shifting of movement, as we would experience in real life.

Our brains are hardwired to detect objects and assign value and meaning.

To explain this more simply, humans don’t think like computers and computers, until now, haven’t had the ability to understand humans. At least not easily.

Scientists working on this problem have identified a number of semantic gaps that have prevented this from happening:

  1. The semantic gap between different data sources – structured or unstructured
  2. The semantic gap between the operational data and the human interpretation of this data
  3. The semantic gap between people communicating about a certain information concept.

The Mindaptiv Approach to Closing these Gaps 

The Mindaptiv approach is to turn every object into a set of instructions using a system for automatic object detection. This involves a process for dynamic down-sampling and up-sampling what it sees.

In doing so, every object is converted into a description, and the file size for that description is exponentially smaller than the data itself. This means that every server, laptop, tablet, and smartphone can easily be converted into a Semantically Intelligent device.

For example, a video is converted automatically and seamlessly from pixels into objects with attributes like size, shape, and color, with corresponding information about its time and space coordinates, just like our brains do.

Unlike Artificial Intelligence (AI), that requires a super computer like Watson, Semantic Intelligence, with its diminutive file structures, takes far less processing power and bandwidth. For this reason, high definition images and video, can be stored, transmitted, and presented from semantic definitions at a fraction of the time and cost it would take to send the pixels.

Taking this a few steps further, Semantic Intelligence’s size and speed advantages mean we will be able to send a text in English and have Hindi, Egyptian, or Mandarin come out the other side. Semantic Intelligence would deliver the text in the correct version of the 14 variants of Chinese, while translating between the vernacular of both the communicator and the receiver.

When it comes to the Internet of Things, the flow of “intelligence” from one device to the next will be exponentially greater. And given a few learning cycles, our devices will finally learn to “think like us.”

The pieces I’ve explained so far are only what Ken refers to as, “a few shavings of ice off the iceberg of possibilities.” It’s not even close to being the tip.

Thomas Frey Futurist speaker Mindaptiv

Ken Granville (right) and the rest of the team at Mindaptiv

Describing the Capabilities

One of the first demos we saw was a side-by-side comparison of a low-res photo, a jpeg under 50k file size. With one side showing the current state of the art, any zooming in on the photo resulted in a highly pixelated image.

Using Mindaptiv technology for transmitting a description rather than pixels, that same low res image could be expanded to a stadium-sized image and still maintain its crispness.

This was also demonstrated with several videos. Think about what it would be like to project a video the size of an airline hangar onto a massive wall and still maintain perfect resolution, yet transmitting the information through exponentially smaller file packets.

The second demo was designed to show how its object-capturing and object-manipulation features worked. In this presentation, a video feed of a vase showed how the vase could be selected and stripped away from the rest of its background. The vase was then placed onto a variety of different video backgrounds. In this example, the vase remained part of a live feed, so the vase itself could be repositioned, expanded, or turned sideways in real time.

Features like this will be very appealing to the special effects people in Hollywood and the gaming world.

Additional demos showed the difference in code once an object was reduced to a description. The number of lines of code dropped from thousands to dozens. Once the description file was sent to it’s receiving device, the lines of code once again expanded into its original multi-thousand-line format.

This contraction-expansion feature will have massive implications in everything from big data, to telecom, to Internet security, to new hardware designs.

 

Final Thoughts

Admittedly, what I’ve described so far is not enough to give you an accurate sense of what’s going on here. Even for those working on the technology, the true implications will take years to fully realize.

In my opinion, Mindaptiv is sitting on a loaded powder keg waiting to explode.

Yes, there are still any number of things that can go wrong, and this may be far too disruptive for most computer companies to readily embrace. But from my vantage point, Mindaptiv will transform the business world more significantly than the invention of the computer itself.

This is a revolution. Over time, all devices will become Semantically Intelligent. As a second step, which may happen somewhat concurrently, AI will be layered over the top, with AI adding the thinking, reasoning, decision-making, diagnosing, even feeling elements to the equation. Think of the movie “Her,” only better.

With a Semantic Intelligence layer, AI will be faster, cheaper, and perform better than anything in existence today.

Yes, I may indeed have had one glass too many of the Mindaptiv Kool-Aid. But even if they don’t manage to carry the torch across the finish line, someone else will. And personally, I can’t wait.

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Computing’s Next Big Transformation – Semantic Intelligence

by | Oct 3, 2014 | Business Trends

I was thoroughly intrigued when I found out the Colorado School of Mines in Golden, Colorado was offering a degree in asteroid mining.

Yes, the idea of extracting water, oxygen, minerals, and metals from an asteroid sounds like science fiction to most people, but it’s not that far away.  In fact, Colorado School of Mines’ newly launched “Space Resources” program will help people get in on the ground floor.

After thinking about the proactive nature of this approach, it became abundantly clear how backward thinking most colleges have become.

When colleges decide on a new degree program, they must first recruit instructors, create a new curriculum, and attract students. As a result, the talent churned out of these newly minted programs is the product of a 6-7 year pipeline.

For this reason, anticipatory-thinking institutions really need to be setting their sights on what business and industries will need 7-10 years from now.

The Risk-Averse Nature of Education

When Harvard professor Clayton M. Christensen released his best-selling book, The Innovator’s Dilemma, his core message that disruptive change is the path to success, was only partially embraced by higher education.

While many were experimenting with MOOCs and smart whiteboards, changes in the subject matter of their courses still evolved at the traditional pace of discovery.

This is not to say colleges are not innovative. Rather, the demands of today’s emerging tech environment are forcing business and industries to shift into an entirely new gear. And that most definitely includes our academic institutions.

From a management perspective, it’s far easier to oversee a contained system where all variables are constrained. But during times of change, we tend to give far more power to the “unleashers,” who are determined to test the status quo and release ideas and trial balloons to see what works.

For this reason, managers and creatives often find themselves on opposing sides, and the winners of these warring factions often determine what we as consumers see as the resulting ripples of change.

Offering Pilot Programs

When Facebook bought Oculus Rift in March 2014 for $2 billion, the job boards went crazy, as there was an instant uptick in the demand for VR designers, engineers, and experience creators. But no one was teaching VR, and certainly not the Oculus Rift version of it.

Colleges have a long history of being blindsided by new technologies:

  • When eBay launched, no one was teaching ecommerce strategies
  • When Myspace launched, no one was teaching social networking
  • When Google launched, no one was teaching online search engine strategies
  • When Uber launched, no one was teaching sharing economy business models
  • When Apple first opened their App Store, no one was teaching smart phone app design
  • When Amazon first allowed online storefronts, no one was teaching the Amazon business model
  • When YouTube first offered ways to monetize videos, no one was teaching it

Since most academic institutions are only willing to put their name on programs with long-term viability, the endorsement of half-baked agendas does not come easy. However, that is exactly what needs to be done.

Colleges can no longer afford to remain comfortably behind the curve.

52 Future College Degrees

As a way of priming your thinking on this matter, here are 52 future degrees that forward-thinking colleges could start offering today:

  1. Space Exploration – space tourism planning and management
  2. Space Exploration – planetary colony design and operation
  3.  Space Exploration – next generation space infrastructure
  4. Space Exploration – advanced cosmology and non-earth human habitats
  5. Bioengineering with CRISPR – policy and procedural strategies
  6. Bioengineering with CRISPR – advanced genetic engineering systems
  7. Bioengineering with CRISPR – operational implementations and system engineering
  8. Bioengineering with CRISPR – ethical regulation and oversight
  9. Smart City – autonomous traffic integration
  10. Smart City – mixed reality modeling
  11. Smart City – autonomous construction integration
  12. Smart City – next generation municipal planning and strategy
  13. Autonomous Agriculture – robotic systems
  14. Autonomous Agriculture – drone systems
  15. Autonomous Agriculture – supply chain management
  16. Autonomous Agriculture – systems theory and integration
  17. Swarmbot – design, theory, and management
  18. Swarmbot – system engineering and oversight
  19. Swarmbot – municipal system design
  20. Swarmbot – law enforcement and advanced criminology systems
  21. Cryptocurrency – digital coin economics
  22. Cryptocurrency – crypto-banking system design
  23. Cryptocurrency – regulatory systems and oversight
  24. Cryptocurrency – forensic accounting strategies
  25. Blockchain – design, systems, and applications
  26. Blockchain – blockchain for biological systems
  27. Blockchain – large-scale integration structures
  28. Blockchain – municipal system design strategies
  29. Global Systems – system planning, architecture, and design
  30. Global Systems – large-scale integration strategies
  31. Global Systems – operational systems checks and balance
  32. Global Systems – governmental systems in a borderless digital world
  33. Unmanned Aerial Vehicle - drone film making
  34. Unmanned Aerial Vehicle – command center operations
  35. Unmanned Aerial Vehicle – municipal modeling and planning systems
  36. Unmanned Aerial Vehicle – emergency response systems
  37. Mixed Reality - experiential retail
  38. Mixed Reality – three-dimensional storytelling
  39. Mixed Reality – game design
  40. Mixed Reality – therapeutic systems and design
  41. Advanced Reproductive Systems – designer baby strategies, planning, and ethics
  42. Advanced Reproductive Systems – surrogate parenting policy and approaches
  43. Advanced Reproductive Systems – organic nano structures
  44. Advanced Reproductive Systems – clone engineering and advanced processes
  45. Artificial Intelligence – data management in an AI environment
  46. Artificial Intelligence – advanced human-AI integration
  47. Artificial Intelligence – streaming AI data services
  48. Artificial Intelligence – advanced marketing with AI
  49. Quantum Computing – data strategies in a quantum-connected world
  50. Quantum Computing – quantum-level encryption and security
  51. Quantum Computing – quantum computing implementation strategies
  52. Quantum Computing – AI-quantum system integration

Final Thought

More so than any time in history, we have a clear view of next generation technologies. Naturally, we’re still a long way from 100% clarity, but for most of the technologies listed above, the shifting tectonic plates of change can be felt around the world.

Without taking decisive action, colleges run the risk of being circumvented by new types of training systems that can meet market demands in a fraction of the time it takes traditional academia to react.

The ideas I’ve listed are a tiny fraction of what’s possible when it comes to emerging tech degrees. Should colleges stick their neck out like Colorado School of Mines and offer degrees that may not be immediately useful? Adding to that question, how many college degrees are immediately useful today?

I’d love to hear your thoughts on this topic.

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