Daily Archives: April 17, 2017

Executive Summary

Artificial intelligence (AI) is increasingly emerging in applications like autonomous vehicles and medical assistance devices, but consumers dont necessarily trust these applications. Research shows that operational safety and data security are decisive factors in getting people to trust new AI technology. Even more important is the balance between control and autonomy in the technology. And communication is key it should be proactive and open in the early stages of introducing the public to the technology. Consumers who can effectively communicate the benefits of an AI application have a reduction in their perceived risk, which results in greater trust, and ultimately, in greater adoption of the technology.

Artificial intelligence (AI) is emerging in applications like autonomous vehicles and medical assistance devices. But even when the technology is ready to use and has been shown to meet customer demands, theres still a great deal of skepticism among consumers. For example, a survey of more than 1,000 car buyers in Germany showed that only 5% would prefer a fully autonomous vehicle. We can find a similar number of skeptics of AI-enabled medical diagnosis systems, such as IBMs Watson. The publics lack of trust in AI applications may cause us to collectively neglect the possible advantages we could gain from them.

In order to understand trust in the relationship between humans and automation, we have to explore trust in two dimensions: trust in the technology and trust in the innovating firm.

How it will impact business, industry, and society.

In human interactions, trust is the willingness to be vulnerable to the actions of another person. But trust is an evolving and fragile phenomenon that can be destroyed even faster than it can be created. Trust is essential to reducing perceived risk, which is a combination of uncertainty and the seriousness of the potential outcome involved. Perceived risk in the context of AI stems from giving up control to a machine. Trust in automation can only evolve from predictability, dependability, and faith.

Three factors will be crucial to gaining this trust: 1.) performance that is, the application performs as expected; 2.) process that is, we have an understanding of the underlying logic of the technology, and 3.) purpose that is, we have faith in the designs intentions. Additionally, trust in the company designing the AI, and the way the way the firm communicates with customers, will influence whether the technology is adopted by customers. Too many high-tech companies wrongly assume that the quality of the technology alone will influence people to use it.

In order to understand how firms have systematically enhanced trust in applied AI, my colleagues Monika Hengstler and Selina Duelli and I conducted nine case studies in the transportation and medical device industries. By comparing BMWs semi-autonomous and fully autonomous cars, Daimlers Future Truck project, ZF Friedrichshafens driving assistance system, as well as Deutsche Bahns semi-autonomous and fully autonomous trains and VAG Nrnbergs fully automated underground train, we gained a deeper understanding of how those companies foster trust in their AI applications. We also analyzed four cases in the medical technology industry, including IBMs Watson as an AI-empowered diagnosis system, HPs data analytics system for automated fraud detection in the healthcare sector, AiCures medical adherence app that reminds patients to take their medication, and the Care-O-bot 3 of Frauenhofer IPA, a research platform for upcoming commercial service robot solutions. Our semi-structured interviews, follow-ups, and archival data analysis was guided by a theoretical discussion on how trust in the technology and in the innovating firm and its communication is facilitated.

Based on this cross-case analysis, we found that operational safety and data security are decisive factors in getting people to trust technology. Since AI-empowered technology is based on the delegation of control, it will not be trusted if it is flawed. And since negative events are more visible than positive events, operational safety alone is not sufficient for building trust. Additionally, cognitive compatibility, trialability, and usability are needed:

Cognitive compatibility describes what people feel or think about an innovation as it pertains to their values. Users tend to trust automation if the algorithms are understandable and guide them toward achieving their goals. This understandability of algorithms and the motives in AI applications directly affect the perceived predictability of the system, which, in turn, is one of the foundations of trust.

Trialability points to the fact that people who were able to visualize the concrete benefits of a new technology via a trial run reduced their perceived risk and therefore their resistance to the technology.

Usability is influenced by both the intuitiveness of the technology, and the perceived ease of use. An intuitive interface can reduce initial resistance and make the technology more accessible, particularly for less tech-savvy people. Usability testing with the target user group is an important first step toward creating this ease of use.

But even more important is the balance between control and autonomy in the technology. For efficient collaboration between humans and machines, the appropriate level of automation must be carefully defined. This is even more important in intelligent applications that are designed to change human behaviors (such as medical devices that incentivize humans to take their medications on time). The interaction should not make people feel like theyre being monitored, but rather, assisted. Appropriate incentives are important to keep people engaged with an application, ultimately motivating them to use it as intended. Our cases showed that technologies with high visibility e.g., autonomous cars in the transportation industry, or AiCure and Care-O-bot in the healthcare industry require more intensive efforts to foster trust in all three trust dimensions.

Our results also showed that stakeholder alignment, transparency about the development process, and gradual introduction of the technology are crucial strategies for fostering trust. Introducing innovations in a stepwise fashion can lead to more gradual social learning, which in turn builds trust. Accordingly, the established firms in our sample tended to pursue a more gradual introduction of their AI applications to allow for social learning, while younger companies such as AiCure tended to choose a more revolutionary introduction approach in order to position themselves as a technology leader. The latter approach has a high risk of rejection and the potential to cause a scandal if the underlying algorithms turn out to be flawed.

If youre trying to get consumers to trust a new AI-enabled application, communication should be proactive and open in the early stages of introducing the public to the technology, as it will influence the companys perceived credibility and trustworthiness, which will influence attitude formation. In the cases we studied, users who could effectively communicate the benefits of an AI application had a reduction in their perceived risk, which resulted in greater trust, and a higher likelihood to adopt the new technology.

Read the original here:

To Get Consumers to Trust AI, Show Them Its Benefits - Harvard Business Review

Amazonians and investors alike, rest assured: Jeff Bezos knows artificial intelligence is a big deal.

The Amazon CEO took a few paragraphs in his annual shareholder letter to explain how the company is using AI throughout its business, having recognized and embraced the external trend. The letter itself focuses on the idea that Amazon is still in Day 1, still a startup thats well aware that irrelevance and excruciating decline begins when you reach Day 2.

Bezos outlined three major categories that artificial intelligence falls into at the company: visible products and moonshots, core operations, and enterprise cloud.

The outside world can push you into Day 2 if you wont or cant embrace powerful trends quickly. If you fight them, youre probably fighting the future. Embrace them and you have a tailwind, Bezos writes.

This category is what Bezos calls a practical application of machine learning, consumer-facing products or services that wouldnt be possible without AI. The Amazon Go grocery store fits in here, as well as Prime Air delivery drones, and of course, the companys virtual personal assistant, Alexa.

While Amazon Go and Prime Air delivery are still in extremely early stages, Alexa has become the virtual assistant du jourthis year we think it won CES without even showing up. Its easy to see Amazon as a leader here, since the Echos release practically invented the product category of voice-assistant speaker.

Artificial intelligence isnt all drones and virtual assistantsBezos says that a lot of the AI work within the company happens under the hood of its core business, e-commerce. The company uses artificial intelligence to predict product demand, power search rankings, create and recommend deals, detect fraud, and translate the site into other languages.

Though less visible, much of the impact of machine learning will be of this type quietly but meaningfully improving core operations, Bezos says.

This also fails to mention Amazons highly-automated warehouses, which use robots to accelerate processing and shipping time.

This is typically where peoples eyes start to glaze over. Its hard to overstate Amazons dominance in the sector, though: the company earns more than 5 times the revenue of its nearest competitor, Microsoft. Alongside S3 storage that supports much of the web (and cripples much of the web when it fails), Amazon offers easy tools for developers to integrate Amazons artificial intelligence algorithms into their own applications, as well as servers for AI companies to run their own algorithms.

The cloud is also a great marketing tool for providers like Amazon, because the work that others are doing on the platform usually sounds very impressive, without the provider having to do much of the tinkering and design that makes the final product work. Bezos touts the companys pre-packaged AI algorithms as helping predict disease and estimate crop yields for farmers, with no machine learning expertise required.

Capping AIs section of the letter, Bezos says: Watch this space. Much more to come.

Read more from the original source:

Jeff Bezos lays out Amazon's three-pronged approach to AI - Quartz

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By Paul Krill

Editor at Large, InfoWorld | Apr 17, 2017

Bonsai's Inkling programming language, which makes it easier to build artificial intelligence applications, is moving closer to a 1.0 release.

Part of the Bonsai Platform for AI, Inkling is aproprietary higher level language that compiles down to Google's open source TensorFlow library for machine intelligence. Inkling is designed to represent AI in terms of what a developer wants to teach the system instead of focusing on low-level mechanics. It abstracts away from dynamic AI algorithms that would otherwise require expertise in machine learning. Declarative and strongly typed, the language resembles a cross between Python and SQL from a syntactic perspective, said Bonsai CEO Mark Hammond.

"Our core focus right now is on enabling enterprises and industrial companies to build control and optimization systems," which could take forms such as advanced robotics, supply chain optimization systems, or oil exploration, Hammond said. A 1.0 release of the language and the Bonsai Platform is targeted for late June. Plans call for eventually promoting development of additional implementations of Inkling. The company itself is focused on making machine learning technologies accessible to developers and engineers without a background in this area but who do have expertise in domain areas where they want to apply the technology.

Bonsai Platform currently is in an early access stage of release. Other components of the it include the Bonsai Artificial Intelligence Engine; command line and web interfaces; and simulators, generators, and data as training sources.

Paul Krill is an editor at large at InfoWorld, whose coverage focuses on application development.

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Originally posted here:

New AI language hides TensorFlow complexity - InfoWorld

Artificial Intelligence is already impacting every industry through automation and machine learning, bringing concerns that AI is on the fast track to replacing many jobs. But these fears aren't new, says Dan Jackson, director of Enterprise Technology at Crestron, a company that designs workplace technology.

"I'd argue this is no different than when we moved from an agricultural to an industrial economy at the turn of the last century. The percentage of people working in agriculture significantly decreased, and it was a big shift, but we still have plenty of jobs 100 years later," he says.

Anytime society experiences a major technological advancement, we need to be prepared for it to change the way we live and work. It's hard to imagine what the future of jobs will look like with AI, but that future exists. And optimists suggest that, like the sewing machine to the textile industry, AI will make us better, more efficient and faster workers.

[ Related story: Think twice before you hire a chief AI officer ]

Antonis Papatsaras, PhD, AI expert and CTO at SpringCM, a contract and document management company, agrees that some concern is warranted, noting it's "consistent with historical reactions to innovation." Similar concerns were voiced during the Industrial Revolution, but they never held up -- instead of replacing jobs, humans were needed to operate the machinery.

"Time after time, we see jobs adapt and shift," he says.

Adam Compain, CEO of ClearMetal, a predictive logistics company, agrees that most fears around AI are disproportional, and -- if we're being honest -- based off movies and TV. Instead of focusing on the fictional "what-ifs" of AI, we should be building strategies to ensure AI doesn't negatively impact employment.

"Artificial Intelligence is named so because it replicates our own way of thinking and, particularly in the application of machine learning, it's a helpful aid in recognizing patterns, managing overwhelming complexity, and handling tasks far too tedious for us to understand," says Compain.

Experts agree that AI has the potential to eliminate mundane, administrative work, while we will always rely on human workers to be empathetic, collaborative, creative and strategic. But it's impact on any industry lies in the hands of the business leaders who are responsible for adopting AI strategies.

Tim Estes, CEO of Digital Reasoning, a cognitive computing company, says that "we cannot reasonably expect the jobs market to remain inflexible to a changing world." Instead, businesses who approach AI with an open mind and embrace the change will find ways to create new jobs, while those who "shun opportunity are most at risk."

[ Related story: How AI, machine learning will impact tech recruiting ]

A recent study of 1,000 global companies by Accenture found that AI is already creating three new categories of jobs: trainers, explainers and sustainers. Trainers are the people who teach AI systems how to act -- whether it's language, human behavior or the intricacies of human interaction. Explainers are the liaison between technology and business leaders, providing more insight and clarity into machine learning for the non-tech workers. Sustainers are the workers required to maintain AI systems and troubleshoot any potential issues.

"Some jobs were highly technical and required advanced degrees, but other roles demanded innately human things such as empathy and interaction. Downstream jobs, such as those in sales, marketing, or service will change to take advantage of the insights from AI, but many of the core skills will remain," says Estes.

It might sound like any job related to AI will require years of technical knowledge, but that isn't the case. We've already seen a shift in tech hiring -- companies often need highly specific skill sets that are hard to find in potential candidates. As a result, more businesses are hiring employees with the right soft skills, and then training them in technical skills.

"This opens a new window of opportunity for a diverse and booming workforce, as many organizations don't necessarily require a college degree from their technical employees. If you onboard a person with a willingness to learn and an understanding of basic technology skills, you can train them on a multitude of systems and applications," says Papatsaras.

Papatsaras also expects to see an overall shift in the education system, where students will be trained from a young age on robotics and AI. It's already happening outside of the education system - games like Minecraft can help teach children the fundamentals of coding to kick start STEM education.

The real takeaway is that any approach to AI will need to consider the human aspect of every business. AI has great potential to increase efficiency and accuracy and it's already been proven in certain industries.

For example, Estes points to the use of AI In banking to identify "rogue traders" and money laundering schemes. It's also improved healthcare by "increasing the speed and accuracy" of cancer diagnosistics. AI can also help reduce the cost and length of human trafficking investigations, a situation where time is precious.

In these examples, argues Estes, AI hasn't replaced jobs, but has positively impacted efficiency. Yet, he still cautions against complacency with AI.

"We need to ensure our education system responds to equip young people with the appropriate skills and adaptability, while businesses and public organizations must invest in training. Perhaps most of all, we need to encourage imagination and willingness to experiment. The organizations that can innovate with AI will reap the benefits. Their growth will make them the primary source of future jobs," he says.

Companies have a choice when implementing AI. They can choose to effectively implement systems that make employee's lives easier and find creative ways to leverage the technology, says Papatsaras. It's up to employers to ease fears for workers around AI and build strategies that benefit everyone.

"At the end of the day, as employers and employees, we need to figure this out. If we play our cards right, AI is here to lessen the burden in our lives and create what we all crave today -- a work-life balance," he says.

Related Video

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Why AI will both increase efficiency and create jobs - CIO

The future of Google lies in its artificial intelligence technologies, CEO Sundar Pichai has said multiple times.

That means Googles products increasingly rely on machine learning programming that allows computers to learn on their own.

Youll notice AI at work in Google Assistant to understand questions you ask, as well as in Google Photos, which uses AI to identify things like objects, animals and people.

But Google also wants developers to use its open source AI software including translation and visual recognition, to build new tools. Google has said making software open source allows outsiders to improve the companys technology.

Google publishes A.I. Experiments to show in simple ways the different things Googles AI software can do. It offers interactive demonstrations of Googles open source technology. The site is aimed at promoting the software and encouraging developers to use it, with some of the code for the experiments available on the site.

The tools are easy to play with if youre not a programmer and can offer a window into what Google is teaching computers to do. Sometimes users interactions with the tools support experiments and development by Google into the future.

At least one of the 10 experiments posted on the site, Quick, Draw!, has played a role in Googles AI research. The widget prompts users to draw a specific thing, like a seesaw, in under 20 seconds. While the user draws, the program tries to guess what the user is drawing.

Google used doodles from Quick, Draw! to teach artificial intelligence software how to draw on its own.

Last week, Google added another drawing tool, this one called AutoDraw, which turns doodles into clip art by comparing them against a database of professional drawings.

Other experiments include:

Continue reading here:

Here's what Google's AI software can do, and how you can help improve it - Recode