Dec 4, 2020

The fantasy of AI: difference between pop culture vs reality

AI
Machine Learning
Rachel Roumeliotis
4 min
AI creators must learn from consumer attitudes and their fascination with science fiction to make technology appealing
AI creators must learn from consumer attitudes and their fascination with science fiction to make technology appealing...

For decades, pop culture and science fiction have illustrated the possibilities of artificial intelligence (AI). However, these perceptions haven’t always been accurate or encouraged positive development. Popular films such as The Matrix, Her, and Ex Machina might have cast a spotlight on the possibilities of AI in the future, but in the long term they have misshaped public perceptions of what AI actually is and how it already surrounds them. 

O’Reilly’s recent survey which gives AI-creators insight into how consumers use and perceive technology depicts just this—a misunderstanding of what AI truly is and how it can help us. 

The rise of AI in popular culture 

When AI is brought up in casual conversation, what do you think comes to mind? Robots? The Terminator? WALL-E? R2D2? The chances are that people think AI is rooted in popular culture, an idea that has been perpetuated by the entertainment industry for over 50 years. 

Most of us have dreamt about self-driving cars and robots that helped us carry out our daily tasks with ease. However, what was once a dream, has now become a reality. Self-driving cars are now real with Elon Musk, Tesla CEO announcing that the electric car maker will now have fully self-driving vehicles on the road by the end of 2020 and robot waiters are now becoming a phenomenon, with the potential of replacing the mundane, repetitive work done by individuals in the service industry.

Television and the big screen have played a large role in introducing AI into our homes, but how does this depiction impact how we develop and implement the technology?

For those working to incorporate AI technology into products and develop new ways to use it, robots and cars are not an everyday focus. The areas of advancement instead look at AI that learns from our actions to more efficiently help us in our day-to-day lives, answering questions for us and completing tasks through speech recognition and language processing at work and at home. 

But how do we harness the excitement around the fantasy of AI to increase everyday adoption?

Much closer to home

One of the best ways to merge the fantasy and reality of AI is to truly understand what consumers think and what they believe is the potential of the technology. 

In our survey, when asked what the most useful form of AI is, more than half (58%) of consumers regarded smart home technology as the most vital. This was closely followed by home security systems (54%), travel recommendations (52%), and virtual assistants (50%). This provides insight into how AI creators can expand their ideas of where AI can be useful to encourage consumers to adopt it in their personal lives. 

While AI is already present in our homes—thanks to smart speakers from Amazon, Apple and Google—more and more consumer groups appreciate the success of smart home technology and are willing to adopt it in the future. 

The future of AI 

Survey respondents were also asked what application of AI excited them the most in the future. Fraud detection (28%) topped the list as the most exciting area for AI development. It was the most commonly cited use by men. This is despite only 11% of consumers closely associating fraud detection with AI. 

While self-driving cars also generated great excitement among 24% of respondents, interestingly, it was the most popular choice among women, younger consumers, and those working in the AI industry by a significant margin (50%). With fraud detection coming out on top, we can start to see the shift from fantasy to practicality, a trend that AI-creators should leverage to reinforce the pragmatic use of AI within the workforce.  

It is up to a wide range of individuals including developers, marketers, product managers and sales to ensure that AI is used and understood correctly. To encourage adoption, developers should focus their efforts on leveraging AI to make consumers’ everyday lives easier, augmenting existing experiences to make them more seamless and exciting. While there might be an indifference with fantasy and reality, more and more consumer groups appreciate the success of smart home technology and are watching the development of autonomous vehicles very closely. It’s up to these sectors to capitalise on the hype, but the results are also a call for the creators of work-focused AI to make solutions that capture the imagination and generate excitement. Not only this, but developers need to have in mind consumer needs relatively clearly even at the start of the process when an idea might be more amorphous. 

Stepping into the present moment 

While AI notions have been driven by popular culture and science fiction throughout our lives, people still don’t understand AI and that it is already everywhere. AI is not solely robots and virtual assistants; it is in our homes and at work even when we least expect it. AI creators must learn from consumer attitudes and their fascination with science fiction to make technology appealing to a wider audience, capitalising on fantasy for long term benefits.  

By Rachel Roumeliotis, Vice President of Data and AI at O’Reilly 

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Jun 23, 2021

Google launches Visual Inspection AI tool for manufacturers

AI
Google
Manufacturing
ML
3 min
Google has launched Visual Inspection AI, a new Google Cloud Platform solution designed to help reduce defects during the manufacturing process

Google Cloud has launched Visual Inspection AI, a new tool to help manufacturers identify defects in products before they're shipped. 

Poor production quality control often leads to significant operational and financial costs. The American Society for Quality estimates that for many organisations this cost of quality is as high as 15-20% of annual sales revenue, or billions of dollars annually for larger manufacturers. Google Cloud’s new Visual Inspection AI solution has been purpose-built for the industry to solve this problem at production scale. 

How does it work? 

The Google Cloud Visual Inspection AI solution automates visual inspection tasks using a set of AI and computer vision technologies that enable manufacturers to transform quality control processes by automatically detecting product defects.

Google built Visual Inspection AI to meet the needs of quality, test, manufacturing, and process engineers who are experts in their domain, but not in AI. 

  • Run autonomously on-premises: Manufacturers can run inspection models at the network edge or on-premises. The inspection can run either in Google Cloud or fully autonomous on your factory shop floor. 
  • Short time-to-value: Customers can deploy in weeks, not the months typical of traditional machine learning (ML) solutions. Built for process and quality engineers, no computer vision or ML experience required. An interactive user interface guides users through all the steps. 
  • Superior computer vision and AI technology: In production trials, Visual Inspection AI customers improved accuracy by up to 10x compared with general-purpose ML approaches, according to benchmarks from several Google Cloud customers. 
  • Get started quickly, with little effort: Visual Inspection AI can build accurate models with up to 300x fewer human-labeled images than general-purpose ML platforms, based on pilots run by several Google Cloud customers.
  • Highly scalable deployment: Manufacturers can flexibly deploy and manage the lifecycle of ML models, scaling the solution across production lines and factories.

Industry use cases

The demo video shows how Visual Inspection AI addresses use cases to solve specific quality control problems in industries such as automotive manufacturing, semiconductor manufacturing, electronics manufacturing and general-purpose manufacturing. 

Kyocera Communications Systems, a manufacturer of mobile phones for wireless service providers, has been able to scale its AI and ML expertise through the use of the solution. “With the shortage of AI engineers, Visual Inspection AI is an innovative service that can be used by non-AI engineers,” said Masaharu Akieda, Division Manager, Digital Solution Division, KYOCERA Communication Systems. “We have found that we are able to create highly accurate models with as few as 10-20 defective images with Visual Inspection AI. We will continue to strengthen our partnership with Google to develop solutions that will lead our customers' digital transformation projects to success.”

Visual Inspection AI has fully integrated with Google Cloud's portfolio of analytics and ML/AI solutions, giving manufacturers the ability to combine its insights with other data sources. The tool integrates with existing products from Google Cloud partners, including SOTEC, Siemens, GFT, QuantiPhi, Kyocer and Accenture. 

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