Readiness for AI

Artificial Intelligence – Are Humans Ready to Accept Its Value?

As promised in my last post,  , here is the first in our roadmap to singularity – a personal view on artificial intelligence [AI].

Artificial intelligence is the ability of a computer to understand what you’re asking and then infer the best possible answer from all the available evidence. Currently, AI is used in the consumer space as a human collaboration tool Siri or Google Now on your iPhone. But there is a frantic race amongst the world’s leading technology giants. AI is being used to enhance and extend human capabilities and providing a simple user interface to cognitive-driven technologies. The implications of true AI are amazing, with the fundamental transformation in AI only occurring in the past five years. For example:

The cognitive computing transformation era really started in 2011, when IBM Watson triumphed at Jeopardy; demonstrating the integration of natural language processing [NLP], machine learning (ML), and big data. This proved that a bundle of cognitive technologies: natural language, artificial intelligence, machine learning and deep learning, analytics and data could be applied to a purposeful context that could be commercialised.

Following on to the introduction of Siri in late 2011, with both Siri and Google Now redefining human-data interaction into a seamless connection with information.

Machine learning is about man assisting computers.

Deep learning is about systems beginning to progress and learn on their own. This is in stark contrast to prior programs which needed to be programmed, in frustratingly in different languages. This programming restricted the capability of the software to the knowledge of the programmers at the time. We are no longer constrained by current requirements – the machine can continue learning on its own; detecting patterns in data, and providing answers before we even know to ask the question.

This detection and interpretation power of AI and cognitive computing is starting to rival what humans can do – and more so.

Recognition technology has also progressed exponentially, with image recognition on Facebook and Google Photos going beyond facial recognition, with algorithms that can now identify what the objects in the photo are. This capability also extends beyond current human capability, and has valuable diagnostic applications in identifying abnormalities on X-rays and MRI scans.

With AI Apps being promulgated across communication technologies, AI is beginning to impact every industry. Education facilities are ramping up curriculums to boost knowledge in these areas, many partnering with technology vendors to open streams of future roles in cognitive computing.


AI Breakthroughs Expected Before 2018

In the next three years we can expect

  • Next-gen A.I. systems will beat the Turing Test [ a way to determine a machine’s ability to exhibit intelligent behaviour indistinguishable from that of a human]. At this time, we need to be prepared for the conversations about the ethical application of these technologies.
  • Human senses (sight, hearing, taste, smell and touch) will all become part of the normal computing experience – as IoT sensors will connect this additional information to machines. As humans, we need to be emotionally and intellectually ready to engage with machines at this level, in a mutual interactive ecosystem.
  • AI will be solving big problems – with machine learning rapidly detecting anomalies in massive big data sets, we will be better prepared to solve some of our major global problems, such as detecting and deterring terrorism, democratising education and managing climate change. We will likely make major scientific discoveries, such as how to better leverage energy resources.Are countries ready to share insight across borders in co-operative ecologies?
  • Healthcare will become more proactive and holistic – instead of approaching single diseases – the focus will be on antiaging at a cellular level, preventing many of the age related illnesses and extending the quality of our ever increasing lifespan. AI and machine learning is already being used in oncology to identify optimal treatment patterns – matching clinical trials with patients. It is supporting diagnostics and robotic surgery, and providing insight into personalised genome-based healthcare.
  • AI will become seamless in normal living –  woven physically and virtually into multiple aspects of our lives – in smart systems of sensors and networks. At what point will the lines of transparency and data privacy be drawn – balancing the value of personalisation and ease of use, with right to use. The value of AI is in its seamless interaction with devices and information, making everything personalised based on translating sensor data into actionable formats consumable by humans at the point of need. Too much need for transparency and privacy threatens the use of its very power.

These are just some of the areas you should be focusing on, both in terms of business opportunities, and in terms of human decisions that need to be made now, ahead of the technology rollout at commercial levels.

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