Although brands strive to optimise the delivery of their product and services, customer experience is a moving target that is much harder to quantify and measure. When selling was done in-person, this was not an issue, because humans are extremely good at gauging customer's expectations and accommodating to all the different customer needs. In the wake of the post-pandemic era, digital commerce is now the new normal. All businesses are shifting online devoid of human touch. This digital shift is not only a response to containing the spread of COVID, but rather a strategic move to operate one's business more efficiently.

Many business AI platforms offer training courses in the specifics of running their architecture and the programming languages needed to develop more AI tools. Businesses that are serious about AI should plan to either hire new employees or give existing ones the time and resources necessary to train in the skills needed to make AI projects succeed.

The federal government's technology standards organization, NIST, has proposed four principles for explainable AI. An esoteric topic this is not. It would be untenable to ask a person or an organization to explain the why behind every decision, but a democratic society is based on the assumption that explanations can be had for all decisions if there is sufficient need. With no meaningful amount of additional resources over time required to do so, it could be argued that on-demand explanations for AI decisions should be available to anyone with a sufficient need to know. The first draft principle that NIST has proposed for public review suggests that AI deliver evidence or reasons for all outputs. Next, algorithms have to make explanations understandable to people occupying all the various roles touching on the system and its actions.

Technology develops differently in Japan. While US tech giants have been grabbing artificial intelligence headlines, a business AI sector has been quietly maturing in Japan, and it is now making inroads into America. Today we sit down again with Miku Hirano, CEO of Cinnamon, and we talk about how exactly this happened. Interestingly, Cinnamon did not start out as an AI company. In fact, when Miku first came on the show, the company had just launched an innovative video-sharing service. Today, we talk about what lead to the pivot to AI and why even a great idea and a great team is no guarantee of success. We also talk about some of the changing attitudes towards startups and women in Japan, the kinds of business practices AI will never change, and Miku give some practical advice for startups going into foreign markets. It's a great discussion, and I think you will really enjoy it. Welcome to Disrupting Japan, straight talk from Japan's most successful entrepreneurs. Today, we're going to sit down and talk about artificial intelligence with Miku Hirano of Cinnamon. Now, Cinnamon is actually a great example of a successful Japanese startup pivot. When we first sat down with Miku four years ago, she had an innovative micro-video sharing company called Tuya and really, you should go back and listen to that episode. I've put a link on the show notes and it was really a good one.

The meteoric rise of artificial intelligence and machine learning in recent years can be attributed largely to the technologies' vast business potential. Google, IBM, Amazon, Facebook, and other tech giants lead the innovation, while enterprises invest lavishly in new tools, analytics, and research. This spending has a profound impact on the trends in AI development, in many ways shaping and steering the course of innovation. Here are the top five of these trends expected to shine in 2020. One of the main obstacles for new commercial AI and ML projects is the amount of prepared high-quality data to train models on.

Many business AI platforms offer training courses in the specifics of running their architecture and the programming languages needed to develop more AI tools. Businesses that are serious about AI should plan to either hire new employees or give existing ones the time and resources necessary to train in the skills needed to make AI projects succeed.

IBM Research labs are part of a tradition where large tech companies had extensive research labs. IBM Research, along with the original Bell labs and the Xerox Palo Alto Research Center (PARC), have developed many innovations. And IBM Research continues that tradition to today. I got to visit IBM's Almaden Research center, nestled in a bucolic part of the south San Jose area, up on a hillside, surrounded with fields of grazing cattle and a thin fog from the Pacific Ocean just over the Santa Cruz mountains. But in that lab a lot of amazing research is underway. This lab is also noted for a critical invention - the Winchester disk drive - that revolutionized storage in mainframe computers, and which eventually scaled down to personal computers.

Recently, I picked up Kai-Fu Lee's newest book, AI Superpowers. Kai-Fu Lee is one of the most plugged-in AI investors on the planet, managing over $2 billion between six funds and over 300 portfolio companies in the US and China. With a foothold in both Beijing and Silicon Valley, Lee looks at the power balance between Chinese and US tech behemoths--each turbocharging new applications of deep learning and sweeping up global markets in the process. In this post, I'll be discussing Lee's "Four Waves of AI," an excellent framework for discussing where AI is today and where it's going. I'll also be featuring some of the hottest Chinese tech companies leading the charge, worth watching right now.

In the first part of this blog, we saw that design principles and digital ethics build trust in artificial intelligence (AI). However, we didn't address the areas where we are most likely to find those ethical challenges. A checklist can help identify the most relevant pitfalls. Such a list must be defined for individual AI tasks and the planning, implementation, operation, and procedures, as well as the factors needed to end an AI system's life. The following questions can help you formulate this type of checklist.

Artificial intelligence (AI) technologies open doors to new possibilities that can enrich (and potentially harm) the lives of many people. Digital ethics can help manage the risks expected from related changes in the labor market and our society. Growing automation may create a world where people have to work only a few hours a week – although it will be a challenge for humanity to get there without social unrest. Support for this trend comes from the tendency to realize intelligent enterprises by using embedded AI systems in production and business management. Personal assistants will be the spearhead of a new generation of user interfaces.