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Artificial intelligence needs more than artificial trust

The technology that makes facial recognition possible is paving the way for machines to recognise feelings, writes ARTHUR GOLDSTUCK



The great irony of artificial intelligence (AI) and devices that recognise our voices, faces and fingerprints is that they are oblivious to our thoughts and feelings.

“We need to rethink our relationship with technology,” says Rana el Kaliouby, co-founder and CEO of an AI company called Affectiva. “Machines know a lot about us but are completely oblivious to our emotional and cognitive states. Yet, AI is going to change only the way we connect with our devices, but it will fundamentally change the way we connect and communicate with human beings.”

She is speaking in a packed out session at Dell Technologies World in Las Vegas, where more than 15,000 paying delegates are receiving a deep dive into topics as diverse as cloud computing and sustainability of the oceans. Her concern is that, as much as machines need to win the trust if humans, so humans must also win the trust of machines. That sounds absurd for inanimate objects, but this form of artificial trustwill be essential in a future where machines will be expected to assess both our identities and our moods, not to mention our needs.

El Kaliouby earned a PhD in machine learning at CambridgeUniversity, and helped found Affectiva in Boston, USA, to put into practice her research.

“I spent the last 20 years working to build algorithms that understand people’s emotional states, cognitive states, and apply them to the technology around us that makes them more effective.”

The reality, she discovered, is that as we imbue machines with greater intelligence, we must also imbue ourselves with a greater ethical mission.

“We need a news social contract between humans and machines. It’s a two way street. Can AI trust humans? And what will it take to have reciprocal trust? There are a lot of examples of where it goes wrong, like the chatbot on Twitter that became racist, a self driving car that kills people, and a face recognition system that discriminates against people, especially women of colour.

“Sometimes trust is explicit, but most times it is implicit, manifested in subtle interactions like tone of voice and facialexpression. The core of that is empathy. People who havehigher empathy tend to be more liable to be trusted andtherefore more persuasive and tend to be more successful in their personal lives.

“We can’t work with people we don’t trust, and I argue it is the same with AI. We have a lot of common intelligence but not enough emotional intelligence. What if a computer can tell the difference between a smile and smirk? Both involve the lower half of face but have very different meanings.”

She gives the example of the contrast between physical healthcare and mental healthcare. When people walk into doctors rooms they don’t ask what their temperature or blood pressure is, they just measure it. But in mental health care, the practitioner must ask, typically on a scale of 1 to 10, how much the person is hurting.

“The science of emotions has been around for over 200 years,since Duchenne de Boulogne mapped out stimulation of human muscles, through to a modern facial action coding system. It takes a 100 hours of training to become a professional facial analyser. It’s very time-intensive, and it’s not scalable. We use machine learning and big data and tons of computing power to speed up that process. Imagine when that becomes instant?”

The most immediate practical application of the technology is likely to be in the automotive sector, and long before self-driving cars become the norm. However, it is with cars that can switch between human-control and self-driving that the technology will come into its own.

“Our system detects four levels of drowsiness. If you are able to detect that in real time, the car can intervene in a number of ways to make it a safer driving experience. It can tell if you are using your cellphone while driving. By detecting eye gaze direction and using object detectors, the system tells us you’re not keeping your eyes on the road and looking at a smartphone. 

“How can a car react if it senses you’re distracted or drowsy? It can start with an alert. If the vehicle is semi-autonomous, it can say ‘I can be a better driver than you’, and it can take over control.

“Ina  few years, with robo-taxis, the car will still need to understand how many people are in the vehicle, what’s the mood in there, are people stressed or enjoying the ride and, if not, how can we craft the riding experience to make it more enjoyable?”

She points out that luxury car brands are in stress, because their marketing message revolves around the driving experience. Once the owner is no longer driving, the experience will still remain the key.

That, however, does not address the subtle ethical concernsthat are somewhat more nuanced than a car killing its passengers. Many supposedly cutting edge systems useCaucasian faces to “train” the algorithms to become intelligent and distinguish between faces. The result is that they have difficulty identifying non-Caucasian faces. Even within this sub-set, however, there are cultural differences that affect expressions. Affectiva addressed the issue from the start.

“We have amassed 5-billion facial frames from around world,” says El Kaliouby. “We collect spontaneous facial expressions as people go about their daily activity, and there are numerous cultural and gender differences. Women are more expressive than men but it differs by culture. So in theUK there is no significant difference, but in the USA there is a 40% difference.

“Our data is diverse, not only by gender and culture, but also context, like wearing glasses, or blurry photos, as well as by gender, age, and race diversity. It’s not perfect, but at least we are thinking about it and trying to avoid accidentally discriminating based on ethnicity.”

• Arthur Goldstuck is founder of World Wide Worx and editor-in-chief of Follow him on Twitter and Instagram on @art2gee


IoT sensors are anything from doctor to canary in mines

Industrial IoT is changing the shape of the mining industry and the intelligence of the devices that drive it



The Internet of Things (IoT) has become many things in the mining industry. A canary that uses sensors to monitor underground air quality, a medic that monitors healthcare, a security guard that’s constantly on guard, and underground mobile vehicle control. It has evolved from the simple connectivity of essential sensors to devices into an ecosystem of indispensable tools and solutions that redefine how mining manages people, productivity and compliance. According to Karien Bornheim, CEO of Footprint Africa Business Solutions (FABS), IoT offers an integrated business solution that can deliver long-term, strategic benefits to the mining industry.

“To fully harness the business potential of IoT, the mining sector has to understand precisely how it can add value,” she adds. “IoT needs to be implemented across the entire value chain in order to deliver fully optimised, relevant and turnkey operational solutions. It doesn’t matter how large the project is, or how complex, what matters is that it is done in line with business strategy and with a clear focus.”

Over the past few years, mining organisations have deployed emerging technologies to help bolster flagging profits, manage increasingly weighty compliance requirements, and reduce overheads. These technologies are finding a foothold in an industry that faces far more complexities around employee wellbeing and safety than many others, and that juggles numerous moving parts to achieve output and performance on a par with competitive standards. Already, these technologies have allowed mines to fundamentally change worker safety protocols and improve working conditions. They have also provided mining companies with the ability to embed solutions into legacy platforms, allowing for sensors and IoT to pull them into a connected net that delivers results.

“The key to achieving results with any IoT or technology project is to partner with service providers, not just shove solutions into identified gaps,” says Bornheim. “You need to start in the conceptual stage and move through the pre-feasibility and bankable feasibility stages before you start the implementation. Work with trained and qualified chemical, metallurgical, mechanical, electrical, instrumentation and structural engineers that form a team led by a qualified engineering lead with experience in project management. This is the only way to ensure that every aspect of the project is aligned with the industry and its highly demanding specifications.”

Mining not only has complexities in compliance and health and safety, but the market has become saturated, difficult and mercurial. For organisations to thrive, they must find new revenue streams and innovate the ways in which they do business. This is where the data delivered by IoT sensors and devices can really transform the bottom line. If translated, analysed and used correctly, the data can provide insights that allow for the executive to make informed decisions about sites, investment and potential.

“The cross-pollination of different data sets from across different sites can help shift dynamics in plant operation and maintenance, in the execution of specific tasks, and so much more,” says Bornheim. “In addition, with sensors and connected devices and systems, mining operations can be managed intelligently to ensure the best results from equipment and people.”

The connection of the physical world to the digital is not new. Many of the applications currently being used or presented to the mining industry are not new either. What’s new is how these solutions are being implemented and the ways in which they are defined. It’s more than sticking on sensors. It’s using these sensors to streamline business across buildings, roads, vehicles, equipment, and sites. These sensors and the ways in which they are used or where they are installed can be customised to suit specific business requirements.

“With qualified electronic engineers and software experts, you can design a vast array of solutions to meet the real needs of your business,” says Bornheim. “Our engineers can programme, create, migrate and integrate embedded IoT solutions for microcontrollers, sensors, and processors. They can also develop intuitive dashboards and human-machine interfaces for IoT and machine-to-machine (M2M) devices to manage the input and output of a wide range of functionalities.”

The benefits of IoT lie in its ubiquity. It can be used in tandem with artificial intelligence or machine learning systems to enhance analytics, improve the automation of basic processes and monitor systems and equipment for faults. It can be used alongside M2M applications to enhance the results and the outcomes of the systems and their roles. And it can be used to improve collaboration and communication between man, machine and mine.

“You can use IoT platforms to visualise mission-critical data for device monitoring, remote control, alerts, security management, health and safety and healthcare,” concludes Bornheim. “The sky is genuinely the limit, especially now that the cost of sensors has come down and the intelligence of solutions and applications has gone up. From real-time insights to hands-on security and safety alerts to data that changes business direction and focus, IoT brings a myriad of benefits to the table.”

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Oracle leads in clash of
e-commerce titans



Three e-commerce platforms have been awarded “gold medals” for leading the way in customer experience. SoftwareReviews, a division of Info-Tech Research Group, named Oracle Commerce Cloud the leader in its 2020 eCommerce Data Quadrant Awards, followed by Shopify Plus and IBM Digital Commerce. The awards are based on user reviews. 
The three vendors received the following citations:

  • Oracle Commerce Cloud ranked highest among software users, earning the number-one spot in many of the product feature section areas, shining brightest in reporting and analytics, predictive recommendations, order management, and integrated search. 
  • Shopify Plus performed consistently well according to users, taking the number-one spot for catalogue management, shopping cart management and ease of customisation.
  • IBM Digital Commerce did exceptionally well in business value created, quality of features, and vendor support.

The SoftwareReviews Data Quadrant differentiates itself with insightful survey questions, backed by 22 years of research in IT. The study involves gathering intelligence on user satisfaction with both product features and experience with the vendor. When distilled, the customer’s experience is shaped by both the software interface and relationship with the vendor. Evaluating enterprise software along these two dimensions provides a comprehensive understanding of the product in its entirety and helps identify vendors that can deliver on both for the complete software experience.

“Our recent Data Quadrant in e-commerce solutions provides a compelling snapshot of the most popular enterprise-ready players, and can help you make an informed, data-driven selection of an e-commerce platform that will exceed your expectations,” says Ben Dickie, research director at Info-Tech Research Group. 

“Having a dedicated e-commerce platform is where the rubber hits the road in transacting with your customers through digital channels. These platforms provide an indispensable array of features, from product catalog and cart management to payment processing to detailed transaction analytics.”

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