“As Chaney Ojinnaka wrote, IoT (Internet of Things) needs AI (Artificial Intelligence). But on the flipside, AI also needs IoT to grow its awareness and understanding of the world. According to Christof Koch, a leading brain researcher at Seattle’s Allen Institute, “Consciousness is a property of matter, like mass or energy.” He says that a strand of grass that turns towards sunlight is more “conscious” than the most advanced computer simulation to date. This is because its information is integrated in a refined physical system that grows and learns as one.
To build machines with a better understanding of the world, we need to build integrated systems that learn through connected sensors and act directly via smart devices. These gadgets expand the physical presence and footprint of AI to help it grow more aware and intelligent. Google’s big bet on mobile and home devices is starting to pay off, while Amazon’s Alexa is already deployed by 7,000 companies in everything from fridges to cars. Each user interaction is now teaching these AIs every moment.
These gadgets provide service and gather data, but also bring additional complexity to the already complicated world. By 2020, the number of connected devices is expected to grow to around 30 billion, according to market analysis firm IHS Markit. Companies need to drive convergence for two major reasons: to reduce the cognitive load on customers, and to streamline design, development, and operations across interfaces.”
Sami Viitamaki. Havas on VentureBeat.
The article above also supplies the following diagram:
Interesting concept. AI needs IoT (as a data collector to improve its cognisance to facilitate learning) as much as IoT needs AI (to handle the volume and variety of messaging to facilitate insight and acceleration).
This draws a parallel with OSS. AI needs OSS (as a data collector to improve its cognisance to facilitate learning) as much as OSS needs AI (to handle the volume and variety of messaging to facilitate insight and acceleration).
OSS are already producing more data than most humans can handle (perhaps they always have). We’ve been through the cycle of using algorithms to augment our decision making process (eg filters, suppressions, augmentations, etc). For the general populace, IoT is the required data collection technique that AI needs. For CSPs, OSS have long captured vast amounts of data on which to learn, but perhaps we haven’t utilised enough sophisticated machine learning (ML) to date.
The touchpoint explosion means that we’ll become even more reliant on sophisticated exponential learning techniques into the future. If you’re looking to add a new skill to your OSS bow over the next few years, I would suggest that AI / ML might be a valuable investment and medium-term differentiator.
What do you think? Can you suggest a more valuable personal development path?
I was listening to a group of telco experts on a podcast the other day. As they were talking, I kept picturing the image of
Earlier in the year, we wrote a series of articles about the chasm that exists between OSS/BSS buyers and sellers (pt1, pt2, pt3, pt4, pt5, pt6). “The chasm” is
Actually, maybe a few related questions. As you’ve seen from recent articles such as this, I’m increasingly interested in the cross-over worlds of comms and
I’d like to share an idea with you. I think it’s a novel idea. However, I’m still trying to work through the possible pros /
Here at Passionate About OSS, we are exactly that – Passionate About OSS (Operational Support Systems). We’re also passionate about BSS, NMS, or any other names you call the tools that help to operationalise your network.
