Will Current AI Interest Produce Spring, or Another Winter?

Does our experience with mobile computing have relevance for our predictions about useful and commercial use cases for artificial intelligence? Possibly. 

Initially, mobile internet, for example, was sort of a “mobile version” of the desktop experience, allowing users to keep doing things they were doing at their desks. But the mobile experiences sometimes were "de-featured" versions of what could be done on desktop devices. Mobile email was a salient exception: mobile utility was higher and ease of use about the same.

The analogy is mobile voice, which allowed communications away from the desk or cordless phone. Lots of value was created.

Then we moved into an era where the mobile device was the preferred device for many use cases, including photo sharing and social media. 

The proliferation of mobile apps was a characteristic of that era, where hotel check-in, for example, was expected to be invoked on a mobile device. 

Now there are many use cases where mobile is itself the native environment, preferred to desktop use cases which do not work as well, if at all.  Turn-by-turn driving instructions or ride-hailing services provide examples. 

source: Medium 

So are there implications for adoption of practical AI? Maybe. 

The difference in the case of AI is that development has not been strictly linear. At least in terms of expectations, we have moved through alternating periods where expectations were greater or lesser. 

It might be quite fair to say that interest in the near-term value of artificial intelligence has moved through periods of inflated expectations before, even if we are at present in a period of high anticipation. 

source: Saegus 

Historically, interest in the immediate value of artificial intelligence has waxed and waned. 

source: Medium

AI Investment Hit an Inflection Point About 2020

Any way you look at it, private investment in artificial intelligence has blossomed. 

source: AI Index Report

AI Will Mostly be Encountered as a Feature of a Product, Not a Product as Such

Whether we encounter artificial intelligence as a user of apps, services or products, we increasingly will be using AI in the future. Very rarely, if ever, will we actually be buying “AI” as a product, though. It will simply be part of some other things we do buy. 

Which is to say we will normally encounter AI as “weak AI,”  some form of machine learning that is optimized for a particular purpose, such as speech recognition, search results, recommendations,  facial recognition or some customer service app. 

This form of AI will be wildly valuable and ubiquitous, early on. 

source: Toolbox 

What we will not often actually use or see is “strong AI” (“ deep AI”),  when machines apply their intelligence to solve complex problems. Still less are we at a stage when “artificial superintelligence” is possible, allowing machines to surpass human intelligence in terms of completing tasks. 

Weak AI will prove very useful as a way of detecting patterns in human or machine behavior that allow decisions to be made. When people use voice assistants such as Siri, they already are engaging in a process supported by weak AI. 

Almost no consumer or business user will ever buy “AI” in a direct sense, as a product. It will be a feature of a product. 

Verizon, Atos Partner on Computer Vision at the Edge

Verizon Business is adding the Atos computer vision to its multi-access edge computing fabric, aiming to support industrial internet of things applications that include the ability to do predictive analysis of industrial process disruptions. 

By 2025, by some estimates, computer vision will be the second-biggest use of artificial intelligence in the manufacturing industries. 

source: Fractovia 

The Atos computer vision platform will analyze 180 billion data points every hour. Using this system, the engineers and operators will be able to pinpoint exactly when and where operation downtime is predicted, up to 30 days in advance, the partners say. 

The Atos platform brings AI-powered video analytics to mission critical environments,  Verizon says. Verizon hopes the Atos BullSequana Edge servers, deployed in Verizon MEC facilities, will  strengthen its 5G edge offers and unlock new use cases. 

The solution is available to customers across a variety of industries including transport, industrials, logistics and manufacturing, Verizon says.  

The Atos Computer Vision Platform enables organizations to process and analyze massive amounts of complex video and image data in real-time to automatically monitor, manage and improve working practices, security and surveillance.

The Brain and Implications for AI

21st century artificial intelligence is dominated by deep learning. 

But one of the isues with deep learning is that it’s often completely data-driven. Prior knowledge is not incorporated. Adding prior knowlege reduces computation, but adds an element of judgment or subjectivity. 

And that will be an issue. We can go faster when we incorporate what we believe we already know. That's why we have "rules of thumb."

But humans process and perceive in ways that are not strictly based on "what exists in reality" but how we conclude things are in reality. We sometimes construct it, in other words. And humans are able to understand and sort through lots of abstractions we might just call "common sense."

Algorithms cannot do this unless they are taught. The "data" does not necessarily help. It's kind of how we had issues with machine vision. "Objects" humans easily recognized often were difficult for AI to perceive. We had to embed that knowledge in the algorithms. 

It all matters for applied AI since AI is about inferences. And inferences made by humans often involve all sorts of embedded rules that make inference generation easier and more accurate. 

AI is Becoming a Feature of Computing and Connectivity

Even when artificial intelligence is more a feature than a product, AI is intertwined with computing and connectivity these days. 

Digital Realty announced the official opening of its first data center in South Korea, said to be the first carrier-neutral facility in the country. Digital Seoul 1 (ICN10) is a multi-story facility spanning 22,000 square feet and is strategically located in the northwest region of Seoul within the Sangam Digital Media City, a newly developed urban planning zone populated with technology and media companies, serving as a hub to promote South Korea's digital economy, Digital Realty says.

With 12 megawatts of capacity, the new facility offers enterprises superior connectivity with direct access to all local exchange carriers in the Korean market as well as the global ecosystem of 4,000 participants in nearly 50 metros across 25 countries.

ICN10 is also a NVIDIA-certified colocation provider of choice in South Korea as part of the NVIDIA DGX-Ready Data Center program. The facility is designed to handle Artificial Intelligence (AI) and Machine Learning (ML) workloads and analytics from NVIDIA.

AI in Mobile Radio Access Networks Gets Results, Firm Claims

Artificial intelligence will be applied to manage mobile radio access networks, it is safe to say. 

“A leading communications service provider in China achieved an 18 percent improvement in downlink throughput ratio using Capacity Turbo in a trial in Guangzhou run across a 1755 cell, 580 base station network,” says Capacity Turbo. “Capacity Turbo also enabled the same CSP to achieve an 81 percent improvement in downlink packet loss and a 22 percent improvement in uplink packet loss across the same network.”

“In another example, the CSP used Capacity Turbo to optimize VoLTE performance and achieved an 81 percent reduction in packet loss while reducing uplink packet loss by around 22 percent,” the company says.

A major CSP in Thailand was able to improve coverage as well as increase base station throughput by between 13 percent to 15 percent, the firm claims.

Also, a CSP in Spain experienced a 15 percent improvement in average user downlink throughput across an optimized 766 cell portion of its network.