Disclaimer: These are my own personal views. I do not speak for my employer. If you quote this article, please have the honesty to present these ideas as what they are: personal, speculative opinions, to be judged on their own merits. If you were around in the 1980s and 1990s, you may remember the now-extinct phenomenon of "computerphobia". I have personally witnessed it a few times as late as the early 2000s -- as personal computers were introduced into our lives, in our workplaces and homes, quite a few people would react with anxiety, fear, or even aggressivity. While some of us were fascinated by computers and awestruck by the potential they could glimpse in them, most people didn't understand them. They felt alien, abstruse, and in many ways, threatening.

South Korean electronics giant Samsung will make France its third global research and development hub in artificial intelligence after Korea and the United States and hire about one hundred experts in Paris, the French president's office said on Wednesday. The announcement, made after a meeting between Emmanuel Macron and Samsung's strategy chief Young Sohn in Paris, comes as the French president prepares to unveil a major plan to boost the country's capacities in AI on Thursday. Samsung Electronics will build up its current team of 15 people in the Paris area to 50 scientists by the end of the year, before reaching more than 100 experts eventually, two presidential advisors said. Samsung France did not immediately return a request for comment. Japan's Fujitsu will also make France its number one European research center for AI, strengthening a team of 15 people currently hosted at the country's elite engineering school Polytechnique near Paris.

President Emmanuel Macron is rolling out France's strategy to close an artificial-intelligence gap with the U.S. and China, saying it'll require Europeans to get more comfortable with sharing data. After a buildup that includes naming one of the world's top mathematicians as his point man, Macron is expected to propose higher government funding for research and updated rules on data use in his speech Thursday at a former 13th-century Cistercian college in Paris. He also plans to tout incentives for scientists and companies to move to France. "With the digital revolution, we must be at the side of French industrial companies in the disruptive dynamic of innovation," Macron told French business executives this week. European powers risk falling behind in the global competition for future technologies as Google, Apple, Facebook and China's AI companies plow ahead.

On a crisp Saturday morning in Orsay, a southwestern suburb of Paris with some 16,500 inhabitants, the rue de Paris was bustling. But while many residents were doing their usual weekend shopping at the fishmonger or the butcher shop, further up the street, in a small former chateau that is now the town's cultural center, about 80 people had set aside their late-morning hours to hear the "voeux" of their legislative representative to the National Assembly, Cédric Villani. The voeux, or "new year's wishes," are a standard exercise of French politicians from the president on down, in which they review activities of the past year and lay out projects for the year to come. Villani, a mathematician and Fields Medal winner (often shorthanded as the equivalent of the Nobel Prize in mathematics), was new to the practice; only six months earlier, he was still an academic. He was dressed as always -- winter or summer -- in a black three-piece suit, a shirt with cufflinks, a spider brooch on his lapel, and a large, floppy tie called a lavallière (today's version in purple).

How do I become more effective at leveraging (big) data and analytics (artificial intelligence) to power my business? Success with artificial intelligence doesn't begin with technology, but rather the business, and more specifically the people and processes running the business. Before deploying technology, leaders should seek to understand (envision) how artificial intelligence could power a profitable business, and drive compelling customer and operational outcomes. Collaboration with stakeholders and key constituents is critical to understanding the decisions and needs of the business. While every organization's needs vary, there exists a consistent, transparent process that can drive a more stable and widespread adoption of artificial intelligence.

Many executives in all branches of insurance underestimate the disruption that will occur -- and the new talent that is needed. The disruptive power of digital technologies has spread more slowly across the insurance industry than other financial services. This will not last much longer, and many insurance executives risk being caught by surprise by the drastic changes these advanced technologies will inspire. What kind of change is coming? In life insurance, a U.S. company says it can help companies accept or reject new policies by analyzing selfies to determine an applicant's health. In other examples, advanced analytics can help fine-tune prices and segment customers more accurately; machine learning can present precise cross-selling opportunities; and digital interfaces can support single-event policies and purchases without any interaction with human agents. Indeed, the first waves of disruption have already hit automotive insurance, where claims are being processed using smartphone apps and where online aggregators are leading buyers to the lowest-priced offers from a range of companies.

Darktrace, the leader in Enterprise Immune System technology, has today announced that Swope Health Services has deployed its machine learning platform into the core of its network to detect cyber-threats at their earliest stages. Founded in 1969, Swope Health Services provides primary care, mental health programs, and community outreach to the broader Kansas City area. "Cyber-attacks are evolving at an unprecedented rate and healthcare organizations continue to be a top target," commented Brian Thomas, CIO, Swope Health Services. "Darktrace's ability to self-learn our network and prioritize the anomalies within our infrastructure is game-changing. Because its AI technology doesn't look at yesterday's attack to predict that of tomorrow, it has the unique ability to find potential threats that have never been seen before."

A look under the hood of any major search, commerce, or social-networking site today will reveal a profusion of "deep-learning" algorithms. Over the past decade, these powerful artificial intelligence (AI) tools have been increasingly and successfully applied to image analysis, speech recognition, translation, and many other tasks. Indeed, the computational and power requirements of these algorithms now constitute a major and still-growing fraction of datacenter demand. Designers often offload much of the highly parallel calculations to commercial hardware, especially graphics-processing units (GPUs) originally developed for rapid image rendering. These chips are especially well-suited to the computationally intensive "training" phase, which tunes system parameters using many validated examples.

Originally published in French as Phénoménologie de la Perception (1962/1945).

Lucas asserted "... technology-enhanced teaching and learning can dramatically improve the quality and success of higher education ..." His Figure 1 and Figure 2, in outlining traditional versus technology-enhanced courses, suggested traditional teaching methods deliver a low-quality result, while professional (Hollywood) production methods deliver a high-quality result, with, again, no evidence provided. The idea of universities as "content producers" giving students "content" consisting of "course materials and exercises" gave me an analogous idea. Families give food and clothing to their children, but families are inefficient and can involve bloated administrations (parents). Just as parents do more than feed (they try to create an environment where their children can develop and thrive), universities likewise try to create a learning environment for students. Indispensable elements include laboratory work, fieldwork, real essays marked by real scholars (not against a list of bullet points), and project work.